add markdown report for gpu benchmark

This commit integrates the GPU-based channel simulation pipeline into
the ulsim.

A `--cuda` command-line flag is added to enable the GPU path at
runtime. When activated, the simulator will:
- Allocate all necessary GPU and pinned host memory via the
  `init_cuda_chsim_buffers` helper function.
- In the main simulation loop, call the `run_channel_pipeline_cuda`
  function to perform the channel simulation on the GPU, bypassing the
  CPU-based functions.
- Clean up all CUDA resources on exit using the
  `free_cuda_chsim_buffers` helper.
The cpu path is also updated to use multipath_channel_float and add_noise_float.

CMakeLists.txt

@@ -23,6 +23,38 @@ cmake_minimum_required (VERSION 3.16)
 project (OpenAirInterface LANGUAGES C CXX)
 set(CMAKE_CXX_STANDARD 17)
 
+option(CUDA_ENABLE "Enable CUDA accelerated channel simulation" OFF)
+if(CUDA_ENABLE)
+    find_package(CUDA REQUIRED)
+    find_package(CUDAToolkit REQUIRED)
+    message(STATUS "CUDA explicitly enabled, building with GPU acceleration support.")
+
+    enable_language(CUDA)
+
+    if(CMAKE_SYSTEM_PROCESSOR STREQUAL "aarch64")
+        set(CMAKE_CUDA_ARCHITECTURES 90)
+    endif()
+
+    option(USE_UNIFIED_MEMORY "Enable CUDA Unified Memory path" OFF)
+    option(USE_ATS_MEMORY "Enable ATS Hybrid Memory path" ON)
+
+    if(USE_UNIFIED_MEMORY AND USE_ATS_MEMORY)
+        message(FATAL_ERROR "Cannot enable both USE_UNIFIED_MEMORY and USE_ATS_MEMORY at the same time.")
+    endif()
+
+    if(USE_UNIFIED_MEMORY)
+        add_compile_definitions(USE_UNIFIED_MEMORY)
+        message(STATUS "CUDA Unified Memory path enabled.")
+    elseif(USE_ATS_MEMORY)
+        add_compile_definitions(USE_ATS_MEMORY)
+        message(STATUS "CUDA ATS Hybrid Memory path enabled.")
+    else()
+        message(STATUS "CUDA Explicit Copy path enabled (default ATS was overridden).")
+    endif()
+
+    add_compile_definitions(ENABLE_CUDA)
+endif()
+
 #########################################################
 # Base directories, compatible with legacy OAI building #
 #########################################################
@@ -267,6 +299,22 @@ if(GIT_FOUND)
   )
 endif()
 
+option(USE_UNIFIED_MEMORY "Enable CUDA Unified Memory path instead of explicit copies" OFF)
+
+option(USE_ATS_MEMORY "Enable Hybrid ATS (CPU->GPU) and Explicit Copy (GPU->CPU) path" OFF)
+
+if(USE_UNIFIED_MEMORY AND USE_ATS_MEMORY)
+  message(FATAL_ERROR "Cannot enable both USE_UNIFIED_MEMORY and USE_ATS_MEMORY at the same time.")
+endif()
+
+if(USE_UNIFIED_MEMORY)
+  add_compile_definitions(USE_UNIFIED_MEMORY)
+  message(STATUS "CUDA Unified Memory path enabled.")
+elseif(USE_ATS_MEMORY)
+  add_compile_definitions(USE_ATS_MEMORY)
+  message(STATUS "CUDA ATS Hybrid Memory path enabled.")
+endif()
+
 # Debug related options
 #########################################
 # asn1c skeletons have hardcoded this flag to make customized debug logs
@@ -1672,6 +1720,17 @@ set (SIMUSRC
 add_library(SIMU STATIC ${SIMUSRC} )
 target_include_directories(SIMU PUBLIC ${OPENAIR1_DIR}/SIMULATION/TOOLS ${OPENAIR1_DIR}/SIMULATION/RF)
 
+option(CHANNEL_SSE "Enable SSE optimizations for channel simulation" OFF)
+if(CHANNEL_SSE)
+    message(STATUS "SSE-optimized channel simulation enabled.")
+    target_compile_definitions(SIMU PRIVATE CHANNEL_SSE)
+endif()
+
+if(CUDA_FOUND)
+  set_property(TARGET SIMU PROPERTY CUDA_ARCHITECTURES ${CMAKE_CUDA_ARCHITECTURES})
+  set_property(TARGET SIMU PROPERTY LINKER_LANGUAGE CXX)
+endif()
+
 include_directories("${NFAPI_DIR}/nfapi/public_inc")
 include_directories("${NFAPI_DIR}/common/public_inc")
 include_directories("${NFAPI_DIR}/pnf/public_inc")
@@ -2050,6 +2109,16 @@ target_link_libraries(nr_srssim PRIVATE
   m pthread ITTI dl nr_ue_phy_meas physim_common
   )
 
+if(CUDA_ENABLE)
+  if (TARGET oai_cuda_lib)
+      target_link_libraries(nr_dlsim PRIVATE oai_cuda_lib)
+      target_link_libraries(nr_ulsim PRIVATE oai_cuda_lib)
+
+      target_compile_definitions(nr_dlsim PRIVATE ENABLE_CUDA)
+      target_compile_definitions(nr_ulsim PRIVATE ENABLE_CUDA)
+  endif()
+endif()
+
 # these simulators do not compile:
 # dlsim_tm7 pbchsim scansim mbmssim pdcchsim pucchsim prachsim syncsim
 foreach(myExe dlsim ulsim)
@@ -2162,3 +2231,9 @@ add_subdirectory(openair2)
 add_subdirectory(openair3)
 add_subdirectory(radio)
 add_subdirectory(tests)
+
+if(TARGET oai_cuda_lib)
+    message(STATUS "CUDA library 'oai_cuda_lib' found, linking to targets...")
+    target_link_libraries(nr_dlsim PRIVATE oai_cuda_lib)
+    target_link_libraries(nr_ulsim PRIVATE oai_cuda_lib)
+endif()

ci-scripts/tests/nr_physim_gpu_benchmark/gpu_performance_baselines.yaml

@@ -0,0 +1,183 @@
+# --- Test Suite 1: Scalability vs. Channel Count ---
+Channels_16_Batch:
+  'Total GPU Time (us)': 5265.72
+  'Avg Time per Channel - GPU (us)': 329.11
+  'Threshold': 100.00
+
+Channels_16_Stream:
+  'Total GPU Time (us)': 5265.72
+  'Avg Time per Channel - GPU (us)': 329.11
+  'Threshold': 100.00
+
+Channels_16_Serial:
+  'Total GPU Time (us)': 5265.72
+  'Avg Time per Channel - GPU (us)': 329.11
+  'Threshold': 100.00
+
+Channels_256_Batch:
+  'Total GPU Time (us)': 85696.71
+  'Avg Time per Channel - GPU (us)': 334.75
+  'Threshold': 100.00
+
+Channels_256_Stream:
+  'Total GPU Time (us)': 85696.71
+  'Avg Time per Channel - GPU (us)': 334.75
+  'Threshold': 100.00
+
+Channels_256_Serial:
+  'Total GPU Time (us)': 85696.71
+  'Avg Time per Channel - GPU (us)': 334.75
+  'Threshold': 100.00
+
+Channels_1024_Batch:
+  'Total GPU Time (us)': 337217.10
+  'Avg Time per Channel - GPU (us)': 329.31
+  'Threshold': 100.00
+
+Channels_1024_Stream:
+  'Total GPU Time (us)': 337217.10
+  'Avg Time per Channel - GPU (us)': 329.31
+  'Threshold': 100.00 
+
+Channels_1024_Serial:
+  'Total GPU Time (us)': 337217.10
+  'Avg Time per Channel - GPU (us)': 329.31
+  'Threshold': 100.00
+
+# --- Test Suite 2: Performance vs. Channel Complexity ---
+Length_16_Batch:
+  'Total GPU Time (us)': 319082.56
+  'Avg Time per Channel - GPU (us)': 311.60
+  'Threshold': 100.00
+
+Length_16_Stream:
+  'Total GPU Time (us)': 319082.56
+  'Avg Time per Channel - GPU (us)': 311.60
+  'Threshold': 100.00
+
+Length_16_Serial:
+  'Total GPU Time (us)': 319082.56
+  'Avg Time per Channel - GPU (us)': 311.60
+  'Threshold': 100.00
+
+Length_64_Batch:
+  'Total GPU Time (us)': 366564.52
+  'Avg Time per Channel - GPU (us)': 357.97
+  'Threshold': 100.00
+
+Length_64_Stream:
+  'Total GPU Time (us)': 366564.52
+  'Avg Time per Channel - GPU (us)': 357.97
+  'Threshold': 100.00
+
+Length_64_Serial:
+  'Total GPU Time (us)': 366564.52
+  'Avg Time per Channel - GPU (us)': 357.97
+  'Threshold': 100.00
+
+Length_128_Batch:
+  'Total GPU Time (us)': 425883.51
+  'Avg Time per Channel - GPU (us)': 415.90
+  'Threshold': 100.00
+
+Length_128_Stream:
+  'Total GPU Time (us)': 425883.51
+  'Avg Time per Channel - GPU (us)': 415.90
+  'Threshold': 100.00 
+
+Length_128_Serial:
+  'Total GPU Time (us)': 425883.51
+  'Avg Time per Channel - GPU (us)': 415.90
+  'Threshold': 100.00
+
+# --- Test Suite 3: Performance vs. MIMO Configuration ---
+MIMO_2x2_Batch:
+  'Total GPU Time (us)': 158999.06
+  'Avg Time per Channel - GPU (us)': 155.27
+  'Threshold': 100.00
+
+MIMO_2x2_Stream:
+  'Total GPU Time (us)': 158999.06
+  'Avg Time per Channel - GPU (us)': 155.27
+  'Threshold': 100.00
+
+MIMO_2x2_Serial:
+  'Total GPU Time (us)': 158999.06
+  'Avg Time per Channel - GPU (us)': 155.27
+  'Threshold': 100.00
+
+MIMO_4x4_Batch:
+  'Total GPU Time (us)': 336377.55
+  'Avg Time per Channel - GPU (us)': 328.49
+  'Threshold': 100.00
+
+MIMO_4x4_Stream:
+  'Total GPU Time (us)': 336377.55
+  'Avg Time per Channel - GPU (us)': 328.49
+  'Threshold': 100.00
+
+MIMO_4x4_Serial:
+  'Total GPU Time (us)': 336377.55
+  'Avg Time per Channel - GPU (us)': 328.49
+  'Threshold': 100.00
+
+MIMO_8x8_Batch:
+  'Total GPU Time (us)': 718312.46
+  'Avg Time per Channel - GPU (us)': 701.48
+  'Threshold': 100.00
+
+MIMO_8x8_Stream:
+  'Total GPU Time (us)': 718312.46
+  'Avg Time per Channel - GPU (us)': 701.48
+  'Threshold': 100.00
+
+MIMO_8x8_Serial:
+  'Total GPU Time (us)': 718312.46
+  'Avg Time per Channel - GPU (us)': 701.48
+  'Threshold': 100.00
+
+# --- Test Suite 4: Performance vs. Signal Samples ---
+Samples_30720_Batch:
+  'Total GPU Time (us)': 84702.49
+  'Avg Time per Channel - GPU (us)': 82.72
+  'Threshold': 100.00
+
+Samples_30720_Stream:
+  'Total GPU Time (us)': 84702.49
+  'Avg Time per Channel - GPU (us)': 82.72
+  'Threshold': 100.00
+
+Samples_30720_Serial:
+  'Total GPU Time (us)': 84702.49
+  'Avg Time per Channel - GPU (us)': 82.72
+  'Threshold': 100.00
+
+Samples_61440_Batch:
+  'Total GPU Time (us)': 168761.94
+  'Avg Time per Channel - GPU (us)': 164.81
+  'Threshold': 100.00
+
+Samples_61440_Stream:
+  'Total GPU Time (us)': 168761.94
+  'Avg Time per Channel - GPU (us)': 164.81
+  'Threshold': 100.00
+
+Samples_61440_Serial:
+  'Total GPU Time (us)': 168761.94
+  'Avg Time per Channel - GPU (us)': 164.81
+  'Threshold': 100.00
+
+Samples_122880_Batch:
+  'Total GPU Time (us)': 336500.40
+  'Avg Time per Channel - GPU (us)': 328.61
+  'Threshold': 100.00
+
+Samples_122880_Stream:
+  'Total GPU Time (us)': 336500.40
+  'Avg Time per Channel - GPU (us)': 328.61
+  'Threshold': 100.00
+
+Samples_122880_Serial:
+  'Total GPU Time (us)': 336500.40
+  'Avg Time per Channel - GPU (us)': 328.61
+  'Threshold': 100.00

ci-scripts/tests/nr_physim_gpu_benchmark/gpu_test_driver.py

@@ -0,0 +1,65 @@
+import argparse
+import os
+import subprocess
+import sys
+import yaml
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Test driver for the GPU benchmark suite.")
+    parser.add_argument("--executable", required=True, help="Path to the test_channel_scalability executable")
+    cli_args = parser.parse_args()
+
+    script_dir = os.path.dirname(os.path.realpath(__file__))
+    matrix_file_path = os.path.join(script_dir, "gpu_test_matrix.yaml")
+
+    try:
+        with open(matrix_file_path) as f:
+            test_matrix = yaml.safe_load(f)
+    except FileNotFoundError:
+        print(f"ERROR: Test matrix file not found at {matrix_file_path}")
+        sys.exit(1)
+
+    failed_tests = []
+
+    for test_case in test_matrix["tests"]:
+        name = test_case["name"]
+        args = test_case["args"]
+
+        print(f"\n{'='*80}")
+        print(f"--- Running Test Case: {name} ---")
+        print(f"{'='*80}\n")
+
+        command = [
+            "python3",
+            os.path.join(script_dir, "run_gpu_benchmark.py"),
+            "--executable",
+            cli_args.executable,
+            "--args",
+            args,
+            "--test-case-name",
+            name,
+        ]
+
+        result = subprocess.run(command)
+
+        if result.returncode != 0:
+            print(f"\n--- !!! Test Case FAILED: {name} !!! ---\n")
+            failed_tests.append(name)
+        else:
+            print(f"\n--- Test Case PASSED: {name} ---\n")
+
+    print(f"\n{'='*80}")
+    print("--- Test Suite Summary ---")
+    if not failed_tests:
+        print("\033[92mAll test cases PASSED.\033[0m")
+        sys.exit(0)
+    else:
+        print(f"\033[91mThe following {len(failed_tests)} test case(s) FAILED:\033[0m")
+        for test_name in failed_tests:
+            print(f"  - {test_name}")
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()

ci-scripts/tests/nr_physim_gpu_benchmark/gpu_test_matrix.yaml

@@ -0,0 +1,76 @@
+tests:
+  # --- Test Suite 1: Scalability vs. Channel Count ---
+  - name: "Channels_16_Batch"
+    args: "-c 16 -t 4 -r 4 -s 122880 -l 32 -m batch -n 25"
+  - name: "Channels_16_Stream"
+    args: "-c 16 -t 4 -r 4 -s 122880 -l 32 -m stream -n 25"
+  - name: "Channels_16_Serial"
+    args: "-c 16 -t 4 -r 4 -s 122880 -l 32 -m serial -n 25"
+  - name: "Channels_256_Batch"
+    args: "-c 256 -t 4 -r 4 -s 122880 -l 32 -m batch -n 25"
+  - name: "Channels_256_Stream"
+    args: "-c 256 -t 4 -r 4 -s 122880 -l 32 -m stream -n 25"
+  - name: "Channels_256_Serial"
+    args: "-c 256 -t 4 -r 4 -s 122880 -l 32 -m serial -n 25"
+  - name: "Channels_1024_Batch"
+    args: "-c 1024 -t 4 -r 4 -s 122880 -l 32 -m batch -n 25"
+  - name: "Channels_1024_Stream"
+    args: "-c 1024 -t 4 -r 4 -s 122880 -l 32 -m stream -n 25"
+  - name: "Channels_1024_Serial"
+    args: "-c 1024 -t 4 -r 4 -s 122880 -l 32 -m serial -n 25"
+
+  # --- Test Suite 2: Performance vs. Channel Complexity ---
+  - name: "Length_16_Batch"
+    args: "-c 1024 -t 4 -r 4 -s 122880 -l 16 -m batch -n 25"
+  - name: "Length_64_Batch"
+    args: "-c 1024 -t 4 -r 4 -s 122880 -l 64 -m batch -n 25"
+  - name: "Length_64_Stream"
+    args: "-c 1024 -t 4 -r 4 -s 122880 -l 64 -m stream -n 25"
+  - name: "Length_64_Serial"
+    args: "-c 1024 -t 4 -r 4 -s 122880 -l 64 -m serial -n 25"
+  - name: "Length_128_Batch"
+    args: "-c 1024 -t 4 -r 4 -s 122880 -l 128 -m batch -n 25"
+  - name: "Length_128_Stream"
+    args: "-c 1024 -t 4 -r 4 -s 122880 -l 128 -m stream -n 25"
+  - name: "Length_128_Serial"
+    args: "-c 1024 -t 4 -r 4 -s 122880 -l 128 -m serial -n 25"
+
+  # --- Test Suite 3: Performance vs. MIMO Configuration ---
+  - name: "MIMO_2x2_Batch"
+    args: "-c 1024 -t 2 -r 2 -s 122880 -l 32 -m batch -n 25"
+  - name: "MIMO_2x2_Stream"
+    args: "-c 1024 -t 2 -r 2 -s 122880 -l 32 -m stream -n 25"
+  - name: "MIMO_2x2_Serial"
+    args: "-c 1024 -t 2 -r 2 -s 122880 -l 32 -m serial -n 25"
+  - name: "MIMO_4x4_Batch"
+    args: "-c 1024 -t 4 -r 4 -s 122880 -l 32 -m batch -n 25"
+  - name: "MIMO_4x4_Stream"
+    args: "-c 1024 -t 4 -r 4 -s 122880 -l 32 -m stream -n 25"
+  - name: "MIMO_4x4_Serial"
+    args: "-c 1024 -t 4 -r 4 -s 122880 -l 32 -m serial -n 25"
+  - name: "MIMO_8x8_Batch"
+    args: "-c 1024 -t 8 -r 8 -s 122880 -l 32 -m batch -n 25"
+  - name: "MIMO_8x8_Stream"
+    args: "-c 1024 -t 8 -r 8 -s 122880 -l 32 -m stream -n 25"
+  - name: "MIMO_8x8_Serial"
+    args: "-c 1024 -t 8 -r 8 -s 122880 -l 32 -m serial -n 25"
+
+  # --- Test Suite 4: Performance vs. Signal Samples ---
+  - name: "Samples_30720_Batch"
+    args: "-c 1024 -t 4 -r 4 -s 30720 -l 32 -m batch -n 25"
+  - name: "Samples_30720_Stream"
+    args: "-c 1024 -t 4 -r 4 -s 30720 -l 32 -m stream -n 25"
+  - name: "Samples_30720_Serial"
+    args: "-c 1024 -t 4 -r 4 -s 30720 -l 32 -m serial -n 25"
+  - name: "Samples_61440_Batch"
+    args: "-c 1024 -t 4 -r 4 -s 61440 -l 32 -m batch -n 25"
+  - name: "Samples_61440_Stream"
+    args: "-c 1024 -t 4 -r 4 -s 61440 -l 32 -m stream -n 25"
+  - name: "Samples_61440_Serial"
+    args: "-c 1024 -t 4 -r 4 -s 61440 -l 32 -m serial -n 25"
+  - name: "Samples_122880_Batch"
+    args: "-c 1024 -t 4 -r 4 -s 122880 -l 32 -m batch -n 25"
+  - name: "Samples_122880_Stream"
+    args: "-c 1024 -t 4 -r 4 -s 122880 -l 32 -m stream -n 25"
+  - name: "Samples_122880_Serial"
+    args: "-c 1024 -t 4 -r 4 -s 122880 -l 32 -m serial -n 25"

ci-scripts/tests/nr_physim_gpu_benchmark/run_gpu_benchmark.py

@@ -0,0 +1,130 @@
+import argparse
+import json
+import os
+import re
+import subprocess
+import sys
+import time
+import yaml
+
+
+def parse_benchmark_output(log_text):
+    metrics = {}
+    pattern = re.compile(r"\|\s*(?P<key>[\w\s\(\)\-]+?)\s*\|\s*(?P<value>[\d\.]+)x?\s*\|")
+    for line in log_text.splitlines():
+        match = pattern.search(line)
+        if match:
+            key = match.group("key").strip()
+            value = float(match.group("value"))
+            metrics[key] = value
+    return metrics
+
+
+def analyze_gpu_log(log_file):
+    util_vals, power_vals = [], []
+    try:
+        with open(log_file, "r") as f:
+            for line in f:
+                if "utilization.gpu" in line:
+                    continue
+                parts = line.strip().split(",")
+                if len(parts) >= 2:
+                    try:
+                        util_vals.append(float(parts[0].strip()))
+                        power_vals.append(float(parts[1].strip()))
+                    except (ValueError, IndexError):
+                        continue
+    except FileNotFoundError:
+        return "N/A", "N/A"
+    if not util_vals or not power_vals:
+        return "N/A", "N/A"
+    avg_util = sum(util_vals) / len(util_vals)
+    max_util = max(util_vals)
+    avg_power = sum(power_vals) / len(power_vals)
+    max_power = max(power_vals)
+    return (f"Avg: {avg_util:.1f}%, Max: {max_util:.1f}%", f"Avg: {avg_power:.1f}W, Max: {max_power:.1f}W")
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Run a single GPU benchmark and compare against a baseline.")
+    parser.add_argument("--executable", required=True, help="Path to the test_channel_scalability executable")
+    parser.add_argument("--args", required=True, help="Arguments to pass to the executable, in quotes")
+    parser.add_argument("--test-case-name", required=True, help="The name of the test case to look up in the baseline file")
+    cli_args = parser.parse_args()
+
+    gpu_log_file = "nvidia-smi.log"
+    monitor_command = f"nvidia-smi --query-gpu=utilization.gpu,power.draw --format=csv,noheader,nounits -l 1 > {gpu_log_file}"
+    monitor_process = subprocess.Popen(monitor_command, shell=True)
+    command = [cli_args.executable] + cli_args.args.split()
+    print(f"Running command: {' '.join(command)}")
+    result = subprocess.run(command, text=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
+    monitor_process.terminate()
+    time.sleep(1)
+
+    if result.returncode != 0:
+        print("--- Benchmark FAILED to run ---")
+        print(result.stdout)
+        print(result.stderr)
+        sys.exit(1)
+
+    print("--- Benchmark Ran Successfully ---")
+    print(result.stdout)
+
+    gpu_util_summary, gpu_power_summary = analyze_gpu_log(gpu_log_file)
+    print("\n--- GPU Monitoring Summary ---")
+    print(f"GPU Utilization: {gpu_util_summary}")
+    print(f"GPU Power Draw:  {gpu_power_summary}")
+
+    measured_metrics = parse_benchmark_output(result.stdout)
+    if not measured_metrics:
+        print("ERROR: Could not parse any metrics from the benchmark's text output.")
+        sys.exit(1)
+
+    try:
+        script_dir = os.path.dirname(os.path.realpath(__file__))
+        baseline_file_path = os.path.join(script_dir, "gpu_performance_baselines.yaml")
+        with open(baseline_file_path) as f:
+            all_baselines = yaml.safe_load(f)
+    except FileNotFoundError:
+        print(f"ERROR: Master baseline file not found at {baseline_file_path}")
+        sys.exit(1)
+
+    baseline_metrics = all_baselines.get(cli_args.test_case_name)
+    if not baseline_metrics:
+        print(f"ERROR: Could not find baseline entry for '{cli_args.test_case_name}' in {baseline_file_path}")
+        sys.exit(1)
+
+    all_metrics_ok = True
+    print("\n--- Performance Validation ---")
+    for key, baseline_value in baseline_metrics.items():
+        if key == "Threshold":
+            continue
+
+        if key in measured_metrics:
+            measured_value = measured_metrics[key]
+            threshold = baseline_metrics.get("Threshold", 0.25)
+            upper_bound = baseline_value * (1 + threshold)
+
+            print(f"Metric: '{key}'")
+            print(f"  - Measured: {measured_value:.2f}")
+            print(f"  - Baseline: {baseline_value:.2f}")
+            print(f"  - Allowed Upper Bound: {upper_bound:.2f}")
+
+            if measured_value > upper_bound:
+                print("  - STATUS: \033[91mFAILED (Exceeded threshold)\033[0m")
+                all_metrics_ok = False
+            else:
+                print("  - STATUS: \033[92mPASSED\033[0m")
+        else:
+            print(f"Metric '{key}' from baseline not found in run output. SKIPPING.")
+
+    if all_metrics_ok:
+        print("\nOverall Performance: PASSED")
+        sys.exit(0)
+    else:
+        print("\nOverall Performance: FAILED due to regression.")
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()

openair1/PHY/TOOLS/tests/CMakeLists.txt

@@ -38,4 +38,44 @@ add_executable(test_sse_intrinsics test_sse_intrinsics.cpp)
 target_link_libraries(test_sse_intrinsics PRIVATE GTest::gtest LOG minimal_lib)
 add_dependencies(tests test_sse_intrinsics)
 add_test(NAME test_sse_intrinsics
-  COMMAND ./test_sse_intrinsics)
+  COMMAND ./test_sse_intrinsics)
+
+if(CUDA_ENABLE)
+add_executable(test_multipath test_multipath.c)
+target_link_libraries(test_multipath PRIVATE UTIL SIMU LOG CONFIG_LIB shlib_loader m oai_cuda_lib)
+
+add_dependencies(tests test_multipath)
+add_test(NAME test_multipath COMMAND test_multipath)
+
+add_executable(test_noise test_noise.c)
+target_link_libraries(test_noise PRIVATE UTIL SIMU PHY_COMMON LOG CONFIG_LIB shlib_loader m oai_cuda_lib)
+
+add_dependencies(tests test_noise)
+add_test(NAME test_noise COMMAND test_noise)
+
+add_executable(test_channel_simulation test_channel_simulation.c)
+target_link_libraries(test_channel_simulation PRIVATE UTIL SIMU PHY_COMMON LOG CONFIG_LIB shlib_loader m oai_cuda_lib)
+
+add_dependencies(tests test_channel_simulation)
+add_test(NAME test_channel_simulation COMMAND test_channel_simulation)
+
+add_executable(test_channel_scalability test_channel_scalability.c)
+target_link_libraries(test_channel_scalability PRIVATE UTIL SIMU PHY_COMMON LOG CONFIG_LIB shlib_loader m oai_cuda_lib)
+
+add_dependencies(tests test_channel_scalability)
+add_test(NAME test_channel_scalability COMMAND test_channel_scalability)
+
+find_package(PythonInterp REQUIRED)
+add_test(
+NAME benchmark_gpu_scaling_suite
+COMMAND ${PYTHON_EXECUTABLE} ${CMAKE_SOURCE_DIR}/ci-scripts/tests/nr_physim_gpu_benchmark/gpu_test_driver.py
+  --executable $<TARGET_FILE:test_channel_scalability>
+)
+
+if (TARGET oai_cuda_lib)
+    target_compile_definitions(test_multipath PRIVATE ENABLE_CUDA)
+    target_compile_definitions(test_noise PRIVATE ENABLE_CUDA)
+    target_compile_definitions(test_channel_simulation PRIVATE ENABLE_CUDA)
+    target_compile_definitions(test_channel_scalability PRIVATE ENABLE_CUDA)
+endif()
+endif()

openair1/PHY/TOOLS/tests/test_channel_scalability.c

@@ -0,0 +1,645 @@
+/*
+ * Licensed to the OpenAirInterface (OAI) Software Alliance under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The OpenAirInterface Software Alliance licenses this file to You under
+ * the OAI Public License, Version 1.1  (the "License"); you may not use this file
+ * except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.openairinterface.org/?page_id=698
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *-------------------------------------------------------------------------------
+ * For more information about the OpenAirInterface (OAI) Software Alliance:
+ *      contact@openairinterface.org
+ */
+
+#include <stdio.h>
+#include <stdlib.h>
+#include <math.h>
+#include <string.h>
+#include <time.h>
+#include <getopt.h>
+
+#include "PHY/TOOLS/tools_defs.h"
+#include "SIMULATION/TOOLS/sim.h"
+#include "SIMULATION/TOOLS/oai_cuda.h"
+#include "common/utils/LOG/log.h"
+#include "common/utils/utils.h"
+#include <cuda_runtime.h>
+
+configmodule_interface_t *uniqCfg = NULL;
+
+void exit_function(const char *file, const char *function, const int line, const char *s, const int assert_not_exit)
+{
+  fprintf(stderr, "Exit function called from %s:%d in %s(). Message: %s\n", file, line, function, s);
+  exit(1);
+}
+
+void generate_random_signal_interleaved(float **sig_interleaved, int nb_ant, int num_samples)
+{
+  for (int i = 0; i < nb_ant; i++) {
+    for (int j = 0; j < num_samples; j++) {
+      sig_interleaved[i][2 * j] = (float)((rand() % 2000) - 1000); // Real part (I)
+      sig_interleaved[i][2 * j + 1] = (float)((rand() % 2000) - 1000); // Imaginary part (Q)
+    }
+  }
+}
+
+channel_desc_t *create_manual_channel_desc(int nb_tx, int nb_rx, int channel_length)
+{
+  channel_desc_t *desc = (channel_desc_t *)calloc(1, sizeof(channel_desc_t));
+  desc->nb_tx = nb_tx;
+  desc->nb_rx = nb_rx;
+  desc->channel_length = channel_length;
+  desc->path_loss_dB = 0.0;
+  desc->channel_offset = 0;
+  int num_links = nb_tx * nb_rx;
+  float path_loss = (float)pow(10, desc->path_loss_dB / 20.0);
+  desc->ch = (struct complexd **)malloc(num_links * sizeof(struct complexd *));
+  for (int i = 0; i < num_links; i++) {
+    desc->ch[i] = (struct complexd *)malloc(channel_length * sizeof(struct complexd));
+    for (int l = 0; l < channel_length; l++) {
+      desc->ch[i][l].r = ((double)rand() / (double)RAND_MAX * 0.1) * path_loss;
+      desc->ch[i][l].i = ((double)rand() / (double)RAND_MAX * 0.1) * path_loss;
+    }
+  }
+  return desc;
+}
+
+void free_manual_channel_desc(channel_desc_t *desc)
+{
+  if (!desc)
+    return;
+  int num_links = desc->nb_tx * desc->nb_rx;
+  for (int i = 0; i < num_links; i++) {
+    if (desc->ch[i])
+      free(desc->ch[i]);
+  }
+  if (desc->ch)
+    free(desc->ch);
+  free(desc);
+}
+
+int main(int argc, char **argv)
+{
+  logInit();
+  randominit(0);
+
+  int num_channels = 1;
+  int nb_tx = 4;
+  int nb_rx = 4;
+  int num_samples = 122880;
+  int channel_length = 32;
+  int num_trials = 50;
+  int sum_outputs = 0;
+  char mode_str[10] = "batch";
+
+  struct option long_options[] = {{"num-channels", required_argument, 0, 'c'},
+                                  {"nb-tx", required_argument, 0, 't'},
+                                  {"nb-rx", required_argument, 0, 'r'},
+                                  {"num-samples", required_argument, 0, 's'},
+                                  {"ch-len", required_argument, 0, 'l'},
+                                  {"trials", required_argument, 0, 'n'},
+                                  {"sum-outputs", no_argument, 0, 'S'},
+                                  {"mode", required_argument, 0, 'm'},
+                                  {"help", no_argument, 0, 'h'},
+                                  {0, 0, 0, 0}};
+
+  int opt;
+  while ((opt = getopt_long(argc, argv, "c:t:r:s:l:n:Sm:h", long_options, NULL)) != -1) {
+    switch (opt) {
+      case 'c':
+        num_channels = atoi(optarg);
+        break;
+      case 't':
+        nb_tx = atoi(optarg);
+        break;
+      case 'r':
+        nb_rx = atoi(optarg);
+        break;
+      case 's':
+        num_samples = atoi(optarg);
+        break;
+      case 'l':
+        channel_length = atoi(optarg);
+        break;
+      case 'n':
+        num_trials = atoi(optarg);
+        break;
+      case 'S':
+        sum_outputs = 1;
+        break;
+      case 'm':
+        strncpy(mode_str, optarg, sizeof(mode_str) - 1);
+        break;
+      case 'h':
+        printf("Usage: %s [options]\n", argv[0]);
+        printf("  -c, --num-channels <N>   Number of parallel channels to simulate (Default: 1)\n");
+        printf("  -t, --nb-tx <N>          Number of transmit antennas (Default: 4)\n");
+        printf("  -r, --nb-rx <N>          Number of receive antennas (Default: 4)\n");
+        printf("  -s, --num-samples <N>    Number of samples (Default: 30720)\n");
+        printf("  -l, --ch-len <N>         Channel length (Default: 32)\n");
+        printf("  -n, --trials <N>         Number of trials for averaging (Default: 50)\n");
+        printf("  -S, --sum-outputs        Enable summation of outputs for interference simulation.\n");
+        printf("  -m, --mode <serial|stream|batch>  GPU execution mode (Default: batch)\n");
+        printf("  -h, --help               Show this help message\n");
+        return 0;
+      default:
+        exit(1);
+    }
+  }
+
+  // --- MEMORY ALLOCATION ---
+  // HOST MEMORY
+  int num_tx_signals = sum_outputs ? num_channels : 1;
+  float *tx_sig_data = malloc(num_tx_signals * nb_tx * num_samples * 2 * sizeof(float));
+  float ***tx_sig_interleaved = malloc(num_tx_signals * sizeof(float **));
+  for (int i = 0; i < num_tx_signals; i++) {
+    tx_sig_interleaved[i] = malloc(nb_tx * sizeof(float *));
+    for (int j = 0; j < nb_tx; j++) {
+      tx_sig_interleaved[i][j] = tx_sig_data + (i * nb_tx + j) * num_samples * 2;
+    }
+  }
+
+  float *rx_multipath_data = malloc(nb_rx * num_samples * 2 * sizeof(float));
+  float **rx_multipath_re_cpu = malloc(nb_rx * sizeof(float *));
+  float **rx_multipath_im_cpu = malloc(nb_rx * sizeof(float *));
+  for (int i = 0; i < nb_rx; i++) {
+    rx_multipath_re_cpu[i] = rx_multipath_data + i * num_samples;
+    rx_multipath_im_cpu[i] = rx_multipath_data + (nb_rx + i) * num_samples;
+  }
+
+  c16_t *output_cpu_data = malloc(num_channels * nb_rx * num_samples * sizeof(c16_t));
+  c16_t ***output_cpu = malloc(num_channels * sizeof(c16_t **));
+  for (int c = 0; c < num_channels; c++) {
+    output_cpu[c] = malloc(nb_rx * sizeof(c16_t *));
+    for (int i = 0; i < nb_rx; i++) {
+      output_cpu[c][i] = output_cpu_data + (c * nb_rx + i) * num_samples;
+    }
+  }
+
+  channel_desc_t **channels = malloc(num_channels * sizeof(channel_desc_t *));
+
+  // Define some realistic default values for the channel model
+  // double sampling_rate = 122.88e6;
+  // double channel_bandwidth = 100e6;
+  // uint64_t center_freq = 3.5e9;
+  // double ue_speed_kmh = 3.0;
+  // double max_doppler = (ue_speed_kmh * 1000.0 / 3600.0) * center_freq / 3e8;
+
+  for (int c = 0; c < num_channels; c++) {
+    // Use the full channel descriptor initialization with all 13 arguments
+    // channels[c] = new_channel_desc_scm(nb_tx,
+    //                                    nb_rx,
+    //                                    TDL_A,                 // channel_model
+    //                                    sampling_rate,
+    //                                    center_freq,
+    //                                    channel_bandwidth,
+    //                                    1e-7,                  // DS_TDL (Delay Spread)
+    //                                    max_doppler,           // maxDoppler
+    //                                    CORR_LEVEL_LOW,        // corr_level
+    //                                    0.0,                   // forgetting_factor
+    //                                    0,                     // channel_offset
+    //                                    0.0,                   // path_loss_dB
+    //                                    -100.0);               // noise_power_dB
+
+    channels[c] = create_manual_channel_desc(nb_tx, nb_rx, channel_length);
+    if (!channels[c]) {
+      fprintf(stderr, "Error creating channel descriptor %d\n", c);
+      exit(1);
+    }
+  }
+
+  // DEVICE MEMORY
+  void *d_tx_sig = NULL, *d_rx_sig = NULL, *d_curand_states = NULL, *h_tx_sig_pinned = NULL, *h_output_sig_pinned = NULL,
+       *d_channel_coeffs_gpu = NULL, **d_individual_gpu_outputs = NULL, *d_summed_gpu_output = NULL;
+  c16_t **output_gpu = NULL;
+
+  void *d_tx_sig_batch = NULL, *d_intermediate_sig_batch = NULL, *d_final_output_batch = NULL, *d_channel_coeffs_batch = NULL;
+  float2 *h_channel_coeffs_batch = NULL;
+  float *h_channel_coeffs = NULL;
+  float2 *h_tx_sig_batch_interleaved = NULL;
+
+  const int max_taps = 256;
+  const int padding_len = max_taps - 1;
+  const int padded_num_samples = num_samples + padding_len;
+
+  // Sizes for batch mode
+  size_t tx_batch_bytes = num_channels * nb_tx * padded_num_samples * sizeof(float2);
+  size_t intermediate_batch_bytes = num_channels * nb_rx * num_samples * sizeof(float2);
+  size_t final_batch_bytes = num_channels * nb_rx * num_samples * sizeof(short2);
+  size_t channel_batch_bytes = num_channels * nb_tx * nb_rx * max_taps * sizeof(float2);
+
+  // Sizes for serial/stream mode
+  size_t tx_bytes = nb_tx * padded_num_samples * 2 * sizeof(float);
+  size_t rx_bytes = nb_rx * num_samples * sizeof(float2);
+  size_t output_bytes = nb_rx * num_samples * sizeof(short2);
+  size_t channel_bytes = nb_tx * nb_rx * max_taps * sizeof(float2);
+
+  if (strcmp(mode_str, "batch") == 0) {
+    h_channel_coeffs_batch = malloc(channel_batch_bytes);
+#if defined(USE_UNIFIED_MEMORY)
+    printf("Memory Mode: Unified Memory\n");
+    cudaMallocManaged(&d_tx_sig_batch, tx_batch_bytes, cudaMemAttachGlobal);
+    cudaMallocManaged(&d_intermediate_sig_batch, intermediate_batch_bytes, cudaMemAttachGlobal);
+    cudaMallocManaged(&d_final_output_batch, final_batch_bytes, cudaMemAttachGlobal);
+    cudaMallocManaged(&d_channel_coeffs_batch, channel_batch_bytes, cudaMemAttachGlobal);
+#elif defined(USE_ATS_MEMORY)
+    printf("Memory Mode: ATS\n");
+    d_tx_sig_batch = malloc(tx_batch_bytes);
+    cudaMalloc(&d_intermediate_sig_batch, intermediate_batch_bytes);
+    cudaMalloc(&d_final_output_batch, final_batch_bytes);
+    cudaMalloc(&d_channel_coeffs_batch, channel_batch_bytes);
+#else
+    printf("Memory Mode: Explicit Copy\n");
+    cudaMalloc(&d_tx_sig_batch, tx_batch_bytes);
+    cudaMalloc(&d_intermediate_sig_batch, intermediate_batch_bytes);
+    cudaMalloc(&d_final_output_batch, final_batch_bytes);
+    cudaMalloc(&d_channel_coeffs_batch, channel_batch_bytes);
+    h_tx_sig_batch_interleaved = (float2 *)malloc(tx_batch_bytes);
+#endif
+
+    if (sum_outputs) {
+      cudaMalloc(&d_summed_gpu_output, final_batch_bytes);
+    }
+
+  } else {
+    // Serial & Stream
+    h_channel_coeffs = malloc(channel_bytes);
+    d_individual_gpu_outputs = malloc(num_channels * sizeof(void *));
+
+#if defined(USE_UNIFIED_MEMORY)
+    printf("Memory Mode: Unified Memory\n");
+    cudaMallocManaged(&d_channel_coeffs_gpu, channel_bytes, cudaMemAttachGlobal);
+    cudaMallocManaged(&d_tx_sig, tx_bytes, cudaMemAttachGlobal);
+    cudaMallocManaged(&d_rx_sig, rx_bytes, cudaMemAttachGlobal);
+    for (int c = 0; c < num_channels; c++) {
+      cudaMallocManaged(&d_individual_gpu_outputs[c], output_bytes, cudaMemAttachGlobal);
+    }
+    if (sum_outputs) {
+      cudaMallocManaged(&d_summed_gpu_output, output_bytes, cudaMemAttachGlobal);
+    }
+    h_tx_sig_pinned = d_tx_sig;
+#elif defined(USE_ATS_MEMORY)
+    printf("Memory Mode: ATS\n");
+    cudaMalloc(&d_channel_coeffs_gpu, channel_bytes);
+    cudaMalloc(&d_rx_sig, rx_bytes);
+    h_tx_sig_pinned = malloc(tx_bytes);
+    d_tx_sig = NULL;
+    for (int c = 0; c < num_channels; c++) {
+      cudaMalloc(&d_individual_gpu_outputs[c], output_bytes);
+    }
+    if (sum_outputs) {
+      cudaMalloc(&d_summed_gpu_output, output_bytes);
+    }
+    if (strcmp(mode_str, "serial") == 0) {
+      h_output_sig_pinned = malloc(output_bytes);
+    }
+#else
+    printf("Memory Mode: Explicit Copy\n");
+    cudaMalloc(&d_channel_coeffs_gpu, channel_bytes);
+    cudaMalloc(&d_tx_sig, tx_bytes);
+    cudaMalloc(&d_rx_sig, rx_bytes);
+    cudaMallocHost(&h_tx_sig_pinned, tx_bytes);
+    for (int c = 0; c < num_channels; c++) {
+      cudaMalloc(&d_individual_gpu_outputs[c], output_bytes);
+    }
+    if (sum_outputs) {
+      cudaMalloc(&d_summed_gpu_output, output_bytes);
+    }
+    if (strcmp(mode_str, "serial") == 0) {
+      cudaMallocHost(&h_output_sig_pinned, output_bytes);
+    }
+#endif
+
+    if (strcmp(mode_str, "serial") == 0) {
+      output_gpu = malloc(nb_rx * sizeof(c16_t *));
+      output_gpu[0] = malloc(nb_rx * num_samples * sizeof(c16_t));
+      for (int i = 1; i < nb_rx; i++)
+        output_gpu[i] = output_gpu[0] + i * num_samples;
+    }
+  }
+
+  d_curand_states = create_and_init_curand_states_cuda(nb_rx * num_samples, time(NULL));
+
+  double total_cpu_ns = 0;
+  double total_gpu_ns = 0;
+
+  for (int t = 0; t < num_trials; t++) {
+    if (sum_outputs) {
+      d_individual_gpu_outputs = malloc(num_channels * sizeof(void *));
+    }
+    for (int i = 0; i < num_tx_signals; i++) {
+      generate_random_signal_interleaved(tx_sig_interleaved[i], nb_tx, num_samples);
+    }
+    for (int c = 0; c < num_channels; c++)
+      random_channel(channels[c], 0);
+
+    struct timespec start, end;
+
+    // --- CPU RUN ---
+    clock_gettime(CLOCK_MONOTONIC, &start);
+    for (int c = 0; c < num_channels; c++) {
+      float **current_tx = sum_outputs ? tx_sig_interleaved[c] : tx_sig_interleaved[0];
+      multipath_channel_float(channels[c], current_tx, rx_multipath_re_cpu, rx_multipath_im_cpu, num_samples, 1, 0);
+      add_noise_float(output_cpu[c],
+                      (const float **)rx_multipath_re_cpu,
+                      (const float **)rx_multipath_im_cpu,
+                      0.1,
+                      num_samples,
+                      0,
+                      0,
+                      0,
+                      false,
+                      nb_rx);
+    }
+    if (sum_outputs) {
+      c16_t *final_sum_cpu = calloc(nb_rx * num_samples, sizeof(c16_t));
+      for (int c = 0; c < num_channels; c++) {
+        for (int i = 0; i < nb_rx * num_samples; i++) {
+          final_sum_cpu[i].r += output_cpu[c][0][i].r;
+          final_sum_cpu[i].i += output_cpu[c][0][i].i;
+        }
+      }
+      free(final_sum_cpu);
+    }
+    clock_gettime(CLOCK_MONOTONIC, &end);
+    total_cpu_ns += (end.tv_sec - start.tv_sec) * 1e9 + (end.tv_nsec - start.tv_nsec);
+
+    // --- GPU RUN ---
+    clock_gettime(CLOCK_MONOTONIC, &start);
+    if (strcmp(mode_str, "batch") == 0) {
+      for (int c = 0; c < num_channels; c++) {
+        for (int link = 0; link < nb_tx * nb_rx; link++) {
+          for (int l = 0; l < channel_length; l++) {
+            int batch_idx = (c * nb_tx * nb_rx * max_taps) + (link * max_taps) + l;
+            h_channel_coeffs_batch[batch_idx].x = (float)channels[c]->ch[link][l].r;
+            h_channel_coeffs_batch[batch_idx].y = (float)channels[c]->ch[link][l].i;
+          }
+        }
+      }
+    }
+
+    if (strcmp(mode_str, "batch") == 0) {
+#if defined(USE_UNIFIED_MEMORY) || defined(USE_ATS_MEMORY)
+      float2 *tx_batch_ptr = (float2 *)d_tx_sig_batch;
+      memset(tx_batch_ptr, 0, tx_batch_bytes);
+      for (int c = 0; c < num_channels; c++) {
+        float **current_tx = sum_outputs ? tx_sig_interleaved[c] : tx_sig_interleaved[0];
+        for (int j = 0; j < nb_tx; j++) {
+          float2 *data_start_ptr = tx_batch_ptr + (c * nb_tx + j) * padded_num_samples + padding_len;
+          for (int i = 0; i < num_samples; i++) {
+            data_start_ptr[i] = make_float2(current_tx[j][2 * i], current_tx[j][2 * i + 1]);
+          }
+        }
+      }
+#else // EXPLICIT COPY
+      memset(h_tx_sig_batch_interleaved, 0, tx_batch_bytes);
+      for (int c = 0; c < num_channels; c++) {
+        float **current_tx = sum_outputs ? tx_sig_interleaved[c] : tx_sig_interleaved[0];
+        for (int j = 0; j < nb_tx; j++) {
+          float2 *data_start_ptr = h_tx_sig_batch_interleaved + (c * nb_tx + j) * padded_num_samples + padding_len;
+          for (int i = 0; i < num_samples; i++) {
+            data_start_ptr[i] = make_float2(current_tx[j][2 * i], current_tx[j][2 * i + 1]);
+          }
+        }
+      }
+      cudaMemcpy(d_tx_sig_batch, h_tx_sig_batch_interleaved, tx_batch_bytes, cudaMemcpyHostToDevice);
+#endif
+
+      cudaMemcpy(d_channel_coeffs_batch, h_channel_coeffs_batch, channel_batch_bytes, cudaMemcpyHostToDevice);
+
+      run_channel_pipeline_cuda_batched(num_channels,
+                                        nb_tx,
+                                        nb_rx,
+                                        channel_length,
+                                        num_samples,
+                                        d_channel_coeffs_batch,
+                                        0.1,
+                                        0,
+                                        0xFFFF,
+                                        0xFFFF,
+                                        d_tx_sig_batch,
+                                        d_intermediate_sig_batch,
+                                        d_final_output_batch,
+                                        d_curand_states);
+      cudaDeviceSynchronize();
+
+      if (sum_outputs) {
+        for (int c = 0; c < num_channels; c++) {
+          d_individual_gpu_outputs[c] = d_final_output_batch + c * nb_rx * num_samples * sizeof(short2);
+        }
+        sum_channel_outputs_cuda(d_individual_gpu_outputs, d_summed_gpu_output, num_channels, nb_rx, num_samples);
+      }
+
+    } else {
+      if (strcmp(mode_str, "stream") == 0) {
+        cudaStream_t streams[num_channels];
+        for (int c = 0; c < num_channels; c++)
+          cudaStreamCreateWithFlags(&streams[c], cudaStreamNonBlocking);
+
+        for (int c = 0; c < num_channels; c++) {
+          for (int link = 0; link < nb_tx * nb_rx; link++) {
+            for (int l = 0; l < channels[c]->channel_length; l++) {
+              int idx = link * max_taps + l;
+              ((float2 *)h_channel_coeffs)[idx].x = (float)channels[c]->ch[link][l].r;
+              ((float2 *)h_channel_coeffs)[idx].y = (float)channels[c]->ch[link][l].i;
+            }
+          }
+
+          float *h_tx_ptr = (float *)h_tx_sig_pinned;
+          float **current_tx = sum_outputs ? tx_sig_interleaved[c] : tx_sig_interleaved[0];
+          memset(h_tx_ptr, 0, tx_bytes);
+          for (int j = 0; j < nb_tx; j++) {
+            float *data_start_ptr = h_tx_ptr + (j * padded_num_samples + padding_len) * 2;
+            memcpy(data_start_ptr, current_tx[j], num_samples * 2 * sizeof(float));
+          }
+
+          run_channel_pipeline_cuda_streamed(nb_tx,
+                                             nb_rx,
+                                             channels[c]->channel_length,
+                                             num_samples,
+                                             h_channel_coeffs,
+                                             0.1,
+                                             0,
+                                             0xFFFF,
+                                             0xFFFF,
+                                             d_tx_sig,
+                                             d_rx_sig,
+                                             d_individual_gpu_outputs[c],
+                                             d_curand_states,
+                                             h_tx_sig_pinned,
+                                             d_channel_coeffs_gpu,
+                                             (void *)streams[c]);
+        }
+        if (sum_outputs) {
+          sum_channel_outputs_cuda(d_individual_gpu_outputs, d_summed_gpu_output, num_channels, nb_rx, num_samples);
+        }
+        cudaDeviceSynchronize();
+        for (int c = 0; c < num_channels; c++)
+          cudaStreamDestroy(streams[c]);
+
+      } else if (strcmp(mode_str, "serial") == 0) {
+        for (int c = 0; c < num_channels; c++) {
+          float *h_tx_ptr = (float *)h_tx_sig_pinned;
+          float **current_tx = sum_outputs ? tx_sig_interleaved[c] : tx_sig_interleaved[0];
+
+          memset(h_tx_ptr, 0, tx_bytes);
+          for (int j = 0; j < nb_tx; j++) {
+            float *data_start_ptr = h_tx_ptr + (j * padded_num_samples + padding_len) * 2;
+            memcpy(data_start_ptr, current_tx[j], num_samples * 2 * sizeof(float));
+          }
+
+          for (int link = 0; link < nb_tx * nb_rx; link++) {
+            for (int l = 0; l < channels[c]->channel_length; l++) {
+              int idx = link * max_taps + l;
+              ((float2 *)h_channel_coeffs)[idx].x = (float)channels[c]->ch[link][l].r;
+              ((float2 *)h_channel_coeffs)[idx].y = (float)channels[c]->ch[link][l].i;
+            }
+          }
+
+          void *host_output_ptr_for_pipeline = h_output_sig_pinned;
+#if defined(USE_UNIFIED_MEMORY)
+          host_output_ptr_for_pipeline = d_individual_gpu_outputs[c];
+#endif
+
+          run_channel_pipeline_cuda(output_gpu,
+                                    nb_tx,
+                                    nb_rx,
+                                    channels[c]->channel_length,
+                                    num_samples,
+                                    h_channel_coeffs,
+                                    0.1,
+                                    0,
+                                    0xFFFF,
+                                    0xFFFF,
+                                    0,
+                                    0,
+                                    d_tx_sig,
+                                    d_rx_sig,
+                                    d_individual_gpu_outputs[c],
+                                    d_curand_states,
+                                    h_tx_sig_pinned,
+                                    host_output_ptr_for_pipeline,
+                                    d_channel_coeffs_gpu);
+        }
+      }
+    }
+    cudaDeviceSynchronize();
+    clock_gettime(CLOCK_MONOTONIC, &end);
+    total_gpu_ns += (end.tv_sec - start.tv_sec) * 1e9 + (end.tv_nsec - start.tv_nsec);
+  }
+
+  double avg_cpu_us = (total_cpu_ns / num_trials) / 1000.0;
+  double avg_gpu_us = (total_gpu_ns / num_trials) / 1000.0;
+  double speedup = (avg_cpu_us > 0 && avg_gpu_us > 0) ? (avg_cpu_us / avg_gpu_us) : 0;
+  double avg_cpu_per_channel_us = avg_cpu_us / num_channels;
+  double avg_gpu_per_channel_us = avg_gpu_us / num_channels;
+  double total_samples_processed = (double)num_channels * nb_rx * num_samples;
+  double gpu_throughput_gsps = total_samples_processed / (avg_gpu_us * 1000.0);
+  char val_str[30];
+
+  printf("\n--- Final Benchmark Results ---\n\n");
+  printf("+----------------------------------+--------------------------+\n");
+  printf("| %-32s | %-24s |\n", "Configuration", "Value");
+  printf("+----------------------------------+--------------------------+\n");
+  printf("| %-32s | %-24d |\n", "Parallel Channels", num_channels);
+  snprintf(val_str, sizeof(val_str), "%d x %d", nb_tx, nb_rx);
+  printf("| %-32s | %-24s |\n", "MIMO Configuration (Tx x Rx)", val_str);
+  printf("| %-32s | %-24d |\n", "Signal Length (Samples)", num_samples);
+  printf("| %-32s | %-24d |\n", "Trials per configuration", num_trials);
+  printf("+----------------------------------+--------------------------+\n");
+  printf("| %-32s | %-24s |\n", "Performance Metric", "Value");
+  printf("+----------------------------------+--------------------------+\n");
+  printf("| %-32s | %-24.2f |\n", "Total CPU Time (us)", avg_cpu_us);
+  printf("| %-32s | %-24.2f |\n", "Total GPU Time (us)", avg_gpu_us);
+  printf("| %-32s | %-24.2f |\n", "Avg Time per Channel - CPU (us)", avg_cpu_per_channel_us);
+  printf("| %-32s | %-24.2f |\n", "Avg Time per Channel - GPU (us)", avg_gpu_per_channel_us);
+  snprintf(val_str, sizeof(val_str), "%.2fx", speedup);
+  printf("| %-32s | %-24s |\n", "Speedup (CPU/GPU)", val_str);
+  printf("| %-32s | %-24.3f |\n", "GPU Throughput (GSPS)", gpu_throughput_gsps);
+  printf("+----------------------------------+--------------------------+\n");
+
+  free(tx_sig_data);
+  for (int i = 0; i < num_tx_signals; i++) {
+    free(tx_sig_interleaved[i]);
+  }
+  free(tx_sig_interleaved);
+
+  free(rx_multipath_data);
+  free(rx_multipath_re_cpu);
+  free(rx_multipath_im_cpu);
+
+  free(output_cpu_data);
+  for (int c = 0; c < num_channels; c++) {
+    free(output_cpu[c]);
+    free_manual_channel_desc(channels[c]);
+  }
+  free(output_cpu);
+  free(channels);
+
+  if (strcmp(mode_str, "batch") == 0) {
+    free(h_channel_coeffs_batch);
+    if (sum_outputs) {
+      cudaFree(d_summed_gpu_output);
+    }
+#if defined(USE_ATS_MEMORY)
+    free(d_tx_sig_batch);
+#else
+    cudaFree(d_tx_sig_batch);
+    if (h_tx_sig_batch_interleaved)
+      free(h_tx_sig_batch_interleaved);
+#endif
+    cudaFree(d_intermediate_sig_batch);
+    cudaFree(d_final_output_batch);
+    cudaFree(d_channel_coeffs_batch);
+  } else { // Serial & Stream Cleanup
+    free(h_channel_coeffs);
+    cudaFree(d_channel_coeffs_gpu);
+
+    for (int c = 0; c < num_channels; c++) {
+      cudaFree(d_individual_gpu_outputs[c]);
+    }
+    free(d_individual_gpu_outputs);
+
+    if (sum_outputs) {
+      cudaFree(d_summed_gpu_output);
+    }
+
+#if defined(USE_UNIFIED_MEMORY)
+    cudaFree(d_tx_sig);
+    cudaFree(d_rx_sig);
+#elif defined(USE_ATS_MEMORY)
+    free(h_tx_sig_pinned);
+    cudaFree(d_rx_sig);
+#else // EXPLICIT COPY
+    cudaFreeHost(h_tx_sig_pinned);
+    cudaFree(d_tx_sig);
+    cudaFree(d_rx_sig);
+#endif
+
+    if (strcmp(mode_str, "serial") == 0) {
+      if (h_output_sig_pinned) {
+#if defined(USE_ATS_MEMORY)
+        free(h_output_sig_pinned);
+#elif defined(USE_EXPLICIT_COPY)
+        cudaFreeHost(h_output_sig_pinned);
+#endif
+      }
+      if (output_gpu) {
+        free(output_gpu[0]);
+        free(output_gpu);
+      }
+    }
+  }
+  destroy_curand_states_cuda(d_curand_states);
+
+  printf("Benchmark finished.\n");
+  return 0;
+}

openair1/PHY/TOOLS/tests/test_channel_simulation.c

@@ -0,0 +1,309 @@
+/*
+ * Licensed to the OpenAirInterface (OAI) Software Alliance under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The OpenAirInterface Software Alliance licenses this file to You under
+ * the OAI Public License, Version 1.1  (the "License"); you may not use this file
+ * except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.openairinterface.org/?page_id=698
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *-------------------------------------------------------------------------------
+ * For more information about the OpenAirInterface (OAI) Software Alliance:
+ *      contact@openairinterface.org
+ */
+
+#include <stdio.h>
+#include <stdlib.h>
+#include <math.h>
+#include <string.h>
+#include <time.h>
+
+#include "PHY/TOOLS/tools_defs.h"
+#include "SIMULATION/TOOLS/sim.h"
+#include "SIMULATION/TOOLS/oai_cuda.h"
+#include "common/utils/LOG/log.h"
+#include "common/utils/utils.h"
+#include <cuda_runtime.h>
+
+configmodule_interface_t *uniqCfg = NULL;
+
+void exit_function(const char *file, const char *function, const int line, const char *s, const int assert_not_exit)
+{
+  fprintf(stderr, "Exit function called from %s:%d in %s(). Message: %s\n", file, line, function, s);
+  exit(1);
+}
+
+void generate_random_signal_interleaved(float **sig_interleaved, int nb_ant, int num_samples)
+{
+  for (int i = 0; i < nb_ant; i++) {
+    for (int j = 0; j < num_samples; j++) {
+      sig_interleaved[i][2 * j] = (float)((rand() % 2000) - 1000);
+      sig_interleaved[i][2 * j + 1] = (float)((rand() % 2000) - 1000);
+    }
+  }
+}
+
+channel_desc_t *create_manual_channel_desc(int nb_tx, int nb_rx, int channel_length)
+{
+  channel_desc_t *desc = (channel_desc_t *)calloc(1, sizeof(channel_desc_t));
+  desc->nb_tx = nb_tx;
+  desc->nb_rx = nb_rx;
+  desc->channel_length = channel_length;
+  desc->path_loss_dB = 0.0;
+  desc->channel_offset = 0;
+  int num_links = nb_tx * nb_rx;
+  float path_loss = (float)pow(10, desc->path_loss_dB / 20.0);
+  desc->ch = (struct complexd **)malloc(num_links * sizeof(struct complexd *));
+  for (int i = 0; i < num_links; i++) {
+    desc->ch[i] = (struct complexd *)malloc(channel_length * sizeof(struct complexd));
+    for (int l = 0; l < channel_length; l++) {
+      desc->ch[i][l].r = ((double)rand() / (double)RAND_MAX * 0.1) * path_loss;
+      desc->ch[i][l].i = ((double)rand() / (double)RAND_MAX * 0.1) * path_loss;
+    }
+  }
+  return desc;
+}
+
+void free_manual_channel_desc(channel_desc_t *desc)
+{
+  if (!desc)
+    return;
+  int num_links = desc->nb_tx * desc->nb_rx;
+  for (int i = 0; i < num_links; i++)
+    free(desc->ch[i]);
+  free(desc->ch);
+  free(desc);
+}
+
+int main(int argc, char **argv)
+{
+  logInit();
+  randominit(0);
+
+  int nb_tx_configs[] = {1, 2, 4, 8};
+  int nb_rx_configs[] = {1, 2, 4, 8};
+  int num_samples_configs[] = {30720, 61440, 122880};
+  int channel_length_configs[] = {16, 32};
+  char *channel_type_names[] = {"Short Channel", "Long Channel"};
+  int num_trials = 50;
+  float snr_db = 15.0f;
+
+  printf("Starting Full Channel Pipeline Benchmark (Multipath + Noise)\n");
+  printf("Averaging each test case over %d trials.\n", num_trials);
+  printf(
+      "----------------------------------------------------------------------------------------------------------------------\n");
+  printf("%-15s | %-15s | %-15s | %-20s | %-20s | %-15s\n",
+         "Channel Type",
+         "MIMO Config",
+         "Signal Length",
+         "CPU Pipeline (us)",
+         "GPU Pipeline (us)",
+         "Overall Speedup");
+  printf(
+      "----------------------------------------------------------------------------------------------------------------------\n");
+
+  for (int c = 0; c < sizeof(channel_length_configs) / sizeof(int); c++) {
+    for (int s = 0; s < sizeof(num_samples_configs) / sizeof(int); s++) {
+      for (int m = 0; m < sizeof(nb_tx_configs) / sizeof(int); m++) {
+        int nb_tx = nb_tx_configs[m];
+        int nb_rx = nb_rx_configs[m];
+        int num_samples = num_samples_configs[s];
+        int channel_length = channel_length_configs[c];
+
+        char mimo_str[16];
+        sprintf(mimo_str, "%dx%d", nb_tx, nb_rx);
+
+        channel_desc_t *chan_desc = create_manual_channel_desc(nb_tx, nb_rx, channel_length);
+
+        float **tx_sig_interleaved = malloc(nb_tx * sizeof(float *));
+        float **rx_multipath_re_cpu = malloc(nb_rx * sizeof(float *));
+        float **rx_multipath_im_cpu = malloc(nb_rx * sizeof(float *));
+        c16_t **output_cpu = malloc(nb_rx * sizeof(c16_t *));
+        c16_t **output_gpu = malloc(nb_rx * sizeof(c16_t *));
+
+        for (int i = 0; i < nb_tx; i++) {
+          tx_sig_interleaved[i] = malloc(num_samples * 2 * sizeof(float));
+        }
+        for (int i = 0; i < nb_rx; i++) {
+          rx_multipath_re_cpu[i] = malloc(num_samples * sizeof(float));
+          rx_multipath_im_cpu[i] = malloc(num_samples * sizeof(float));
+        }
+        output_cpu[0] = malloc(nb_rx * num_samples * sizeof(c16_t));
+        output_gpu[0] = malloc(nb_rx * num_samples * sizeof(c16_t));
+        for (int i = 1; i < nb_rx; i++) {
+          output_cpu[i] = output_cpu[0] + i * num_samples;
+          output_gpu[i] = output_gpu[0] + i * num_samples;
+        }
+
+        void *d_tx_sig = NULL, *d_rx_sig = NULL, *d_output_noise = NULL, *d_curand_states = NULL;
+        void *h_tx_sig_pinned = NULL, *h_output_sig_pinned = NULL, *d_channel_coeffs_gpu = NULL;
+
+        const int padding_len = channel_length - 1;
+        const size_t padded_tx_buffer_bytes = nb_tx * (num_samples + padding_len) * 2 * sizeof(float);
+        const size_t rx_buffer_bytes = nb_rx * num_samples * sizeof(float2); // Intermediate buffer
+        const size_t output_buffer_bytes = nb_rx * num_samples * sizeof(short2);
+        const size_t channel_buffer_size = nb_tx * nb_rx * channel_length * sizeof(float2);
+
+#if defined(USE_UNIFIED_MEMORY)
+        cudaMallocManaged(&d_tx_sig, padded_tx_buffer_bytes, cudaMemAttachGlobal);
+        cudaMallocManaged(&d_rx_sig, rx_buffer_bytes, cudaMemAttachGlobal);
+        cudaMallocManaged(&d_output_noise, output_buffer_bytes, cudaMemAttachGlobal);
+        cudaMallocManaged(&d_channel_coeffs_gpu, channel_buffer_size, cudaMemAttachGlobal);
+        h_tx_sig_pinned = d_tx_sig;
+        h_output_sig_pinned = d_output_noise;
+#elif defined(USE_ATS_MEMORY)
+        h_tx_sig_pinned = malloc(padded_tx_buffer_bytes);
+        d_tx_sig = NULL;
+        cudaMalloc(&d_rx_sig, rx_buffer_bytes);
+        cudaMalloc(&d_output_noise, output_buffer_bytes);
+        cudaMalloc(&d_channel_coeffs_gpu, channel_buffer_size);
+        h_output_sig_pinned = malloc(output_buffer_bytes);
+#else
+        cudaMalloc(&d_tx_sig, padded_tx_buffer_bytes);
+        cudaMalloc(&d_rx_sig, rx_buffer_bytes);
+        cudaMalloc(&d_output_noise, output_buffer_bytes);
+        cudaMalloc(&d_channel_coeffs_gpu, channel_buffer_size);
+        cudaMallocHost(&h_tx_sig_pinned, padded_tx_buffer_bytes);
+        cudaMallocHost(&h_output_sig_pinned, output_buffer_bytes);
+#endif
+
+        d_curand_states = create_and_init_curand_states_cuda(nb_rx * num_samples, time(NULL));
+
+        double ts = 1.0 / 30.72e6;
+        float sigma2 = 1.0f / powf(10.0f, snr_db / 10.0f);
+        int num_links = nb_tx * nb_rx;
+        float *h_channel_coeffs = (float *)malloc(num_links * channel_length * sizeof(float2));
+        for (int link = 0; link < num_links; link++) {
+          for (int l = 0; l < channel_length; l++) {
+            int idx = link * channel_length + l;
+            ((float2 *)h_channel_coeffs)[idx].x = (float)chan_desc->ch[link][l].r;
+            ((float2 *)h_channel_coeffs)[idx].y = (float)chan_desc->ch[link][l].i;
+          }
+        }
+
+        double total_cpu_ns = 0;
+        double total_gpu_ns = 0;
+
+        for (int t = 0; t < num_trials; t++) {
+          generate_random_signal_interleaved(tx_sig_interleaved, nb_tx, num_samples);
+          struct timespec start, end;
+
+          const int padding_len = channel_length - 1;
+          const int padded_num_samples = num_samples + padding_len;
+          float *h_tx_ptr = (float *)h_tx_sig_pinned;
+          memset(h_tx_ptr, 0, padded_tx_buffer_bytes);
+          for (int j = 0; j < nb_tx; j++) {
+            float *data_start_ptr = h_tx_ptr + (j * padded_num_samples + padding_len) * 2;
+            memcpy(data_start_ptr, tx_sig_interleaved[j], num_samples * 2 * sizeof(float));
+          }
+
+          clock_gettime(CLOCK_MONOTONIC, &start);
+          float **tx_sig_for_cpu = malloc(nb_tx * sizeof(float *));
+          for (int j = 0; j < nb_tx; j++) {
+            tx_sig_for_cpu[j] = h_tx_ptr + (j * padded_num_samples + padding_len) * 2;
+          }
+          multipath_channel_float(chan_desc, tx_sig_for_cpu, rx_multipath_re_cpu, rx_multipath_im_cpu, num_samples, 1, 0);
+          free(tx_sig_for_cpu);
+          add_noise_float(output_cpu,
+                          (const float **)rx_multipath_re_cpu,
+                          (const float **)rx_multipath_im_cpu,
+                          sigma2,
+                          num_samples,
+                          0,
+                          ts,
+                          0,
+                          false,
+                          nb_rx);
+          clock_gettime(CLOCK_MONOTONIC, &end);
+          total_cpu_ns += (end.tv_sec - start.tv_sec) * 1e9 + (end.tv_nsec - start.tv_nsec);
+
+          clock_gettime(CLOCK_MONOTONIC, &start);
+          run_channel_pipeline_cuda(output_gpu,
+                                    nb_tx,
+                                    nb_rx,
+                                    channel_length,
+                                    num_samples,
+                                    h_channel_coeffs,
+                                    sigma2,
+                                    ts,
+                                    0,
+                                    0,
+                                    0,
+                                    0,
+                                    d_tx_sig,
+                                    d_rx_sig,
+                                    d_output_noise,
+                                    d_curand_states,
+                                    h_tx_sig_pinned,
+                                    h_output_sig_pinned,
+                                    d_channel_coeffs_gpu);
+          cudaDeviceSynchronize();
+          clock_gettime(CLOCK_MONOTONIC, &end);
+          total_gpu_ns += (end.tv_sec - start.tv_sec) * 1e9 + (end.tv_nsec - start.tv_nsec);
+        }
+
+        double avg_cpu_us = (total_cpu_ns / num_trials) / 1000.0;
+        double avg_gpu_us = (total_gpu_ns / num_trials) / 1000.0;
+        double speedup = (avg_gpu_us > 0) ? (avg_cpu_us / avg_gpu_us) : 0;
+
+        printf("%-15s | %-15s | %-15d | %-20.2f | %-20.2f | %-15.2fx\n",
+               channel_type_names[c],
+               mimo_str,
+               num_samples,
+               avg_cpu_us,
+               avg_gpu_us,
+               speedup);
+
+        free(h_channel_coeffs);
+        for (int i = 0; i < nb_tx; i++) {
+          free(tx_sig_interleaved[i]);
+        }
+        for (int i = 0; i < nb_rx; i++) {
+          free(rx_multipath_re_cpu[i]);
+          free(rx_multipath_im_cpu[i]);
+        }
+        free(tx_sig_interleaved);
+        free(rx_multipath_re_cpu);
+        free(rx_multipath_im_cpu);
+        free(output_cpu[0]);
+        free(output_gpu[0]);
+        free(output_cpu);
+        free(output_gpu);
+
+#if defined(USE_UNIFIED_MEMORY)
+        cudaFree(d_tx_sig); // In UM, h_tx_sig_pinned is an alias for d_tx_sig
+        cudaFree(d_rx_sig);
+        cudaFree(d_output_noise);
+#elif defined(USE_ATS_MEMORY)
+        free(h_tx_sig_pinned);
+        cudaFree(d_rx_sig);
+        cudaFree(d_output_noise);
+        free(h_output_sig_pinned);
+#else // EXPLICIT COPY
+        cudaFree(d_tx_sig);
+        cudaFree(d_rx_sig);
+        cudaFree(d_output_noise);
+        cudaFreeHost(h_tx_sig_pinned);
+        cudaFreeHost(h_output_sig_pinned);
+#endif
+
+        cudaFree(d_channel_coeffs_gpu);
+        destroy_curand_states_cuda(d_curand_states);
+        free_manual_channel_desc(chan_desc);
+      }
+    }
+  }
+
+  printf(
+      "----------------------------------------------------------------------------------------------------------------------\n");
+  printf("Pipeline benchmark finished.\n");
+
+  return 0;
+}

openair1/PHY/TOOLS/tests/test_multipath.c

@@ -0,0 +1,289 @@
+/*
+ * Licensed to the OpenAirInterface (OAI) Software Alliance under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The OpenAirInterface Software Alliance licenses this file to You under
+ * the OAI Public License, Version 1.1  (the "License"); you may not use this file
+ * except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.openairinterface.org/?page_id=698
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *-------------------------------------------------------------------------------
+ * For more information about the OpenAirInterface (OAI) Software Alliance:
+ *      contact@openairinterface.org
+ */
+
+#include <stdio.h>
+#include <stdlib.h>
+#include <math.h>
+#include <string.h>
+#include <time.h>
+#include "PHY/TOOLS/tools_defs.h"
+#include "SIMULATION/TOOLS/sim.h"
+#include <cuda_runtime.h>
+#include "SIMULATION/TOOLS/oai_cuda.h"
+#include "common/utils/LOG/log.h"
+#include "common/utils/utils.h"
+
+configmodule_interface_t *uniqCfg = NULL;
+
+void exit_function(const char *file, const char *function, const int line, const char *s, const int assert_not_exit)
+{
+  fprintf(stderr, "Exit function called from %s:%d in %s(). Message: %s\n", file, line, function, s);
+  exit(1);
+}
+
+void generate_random_signal_interleaved(float **sig_interleaved, int nb_ant, int num_samples)
+{
+  for (int i = 0; i < nb_ant; i++) {
+    for (int j = 0; j < num_samples; j++) {
+      sig_interleaved[i][2 * j] = (float)rand() / (float)RAND_MAX * 2.0f - 1.0f;
+      sig_interleaved[i][2 * j + 1] = (float)rand() / (float)RAND_MAX * 2.0f - 1.0f;
+    }
+  }
+}
+
+channel_desc_t *create_manual_channel_desc(int nb_tx, int nb_rx, int channel_length)
+{
+  channel_desc_t *desc = (channel_desc_t *)calloc(1, sizeof(channel_desc_t));
+  desc->nb_tx = nb_tx;
+  desc->nb_rx = nb_rx;
+  desc->channel_length = channel_length;
+  desc->path_loss_dB = 10.0;
+  desc->channel_offset = 0;
+  int num_links = nb_tx * nb_rx;
+  desc->ch = (struct complexd **)malloc(num_links * sizeof(struct complexd *));
+  float path_loss = (float)pow(10, desc->path_loss_dB / 20.0);
+  for (int i = 0; i < num_links; i++) {
+    desc->ch[i] = (struct complexd *)malloc(channel_length * sizeof(struct complexd));
+    for (int l = 0; l < channel_length; l++) {
+      desc->ch[i][l].r = (double)rand() / (double)RAND_MAX * path_loss;
+      desc->ch[i][l].i = (double)rand() / (double)RAND_MAX * path_loss;
+    }
+  }
+  return desc;
+}
+
+void free_manual_channel_desc(channel_desc_t *desc)
+{
+  if (!desc)
+    return;
+  int num_links = desc->nb_tx * desc->nb_rx;
+  for (int i = 0; i < num_links; i++)
+    free(desc->ch[i]);
+  free(desc->ch);
+  free(desc);
+}
+
+int verify_results(float **re_cpu, float **im_cpu, float **re_gpu, float **im_gpu, int nb_rx, int num_samples)
+{
+  double total_error = 0.0;
+  for (int i = 0; i < nb_rx; i++) {
+    for (int j = 0; j < num_samples; j++) {
+      double err_re = re_cpu[i][j] - re_gpu[i][j];
+      double err_im = im_cpu[i][j] - im_gpu[i][j];
+      total_error += (err_re * err_re) + (err_im * err_im);
+    }
+  }
+  double mse = total_error / (nb_rx * num_samples);
+  return (mse < 1e-9) ? 0 : 1;
+}
+
+int main(int argc, char **argv)
+{
+  logInit();
+  randominit(0);
+
+  int nb_tx_configs[] = {1, 2, 4, 8};
+  int nb_rx_configs[] = {1, 2, 4, 8};
+  int num_samples_configs[] = {30720, 61440, 122880};
+  int channel_length_configs[] = {16, 32};
+  char *channel_type_names[] = {"Short Channel", "Long Channel"};
+  int num_trials = 100;
+
+  printf("Starting Multipath Channel Benchmark (CPU vs. Interleaved CUDA)\n");
+  printf("Averaging each test case over %d trials.\n", num_trials);
+  printf(
+      "----------------------------------------------------------------------------------------------------------------------------"
+      "--------------\n");
+  printf("%-15s | %-15s | %-15s | %-15s | %-15s | %-15s | %-10s\n",
+         "Channel Type",
+         "MIMO Config",
+         "Signal Length",
+         "CPU Time (us)",
+         "CUDA Time (us)",
+         "Speedup",
+         "Verification");
+  printf(
+      "----------------------------------------------------------------------------------------------------------------------------"
+      "--------------\n");
+
+  for (int c = 0; c < sizeof(channel_length_configs) / sizeof(int); c++) {
+    for (int s = 0; s < sizeof(num_samples_configs) / sizeof(int); s++) {
+      for (int m = 0; m < sizeof(nb_tx_configs) / sizeof(int); m++) {
+        int nb_tx = nb_tx_configs[m];
+        int nb_rx = nb_rx_configs[m];
+        int num_samples = num_samples_configs[s];
+        int channel_length = channel_length_configs[c];
+
+        char mimo_str[16];
+        sprintf(mimo_str, "%dx%d", nb_tx, nb_rx);
+
+        channel_desc_t *chan_desc = create_manual_channel_desc(nb_tx, nb_rx, channel_length);
+
+        float **s_interleaved = malloc(nb_tx * sizeof(float *));
+        float **r_re_cpu = malloc(nb_rx * sizeof(float *));
+        float **r_im_cpu = malloc(nb_rx * sizeof(float *));
+        float **r_re_gpu = malloc(nb_rx * sizeof(float *));
+        float **r_im_gpu = malloc(nb_rx * sizeof(float *));
+
+        for (int i = 0; i < nb_tx; i++) {
+          s_interleaved[i] = malloc(num_samples * 2 * sizeof(float));
+        }
+        for (int i = 0; i < nb_rx; i++) {
+          r_re_cpu[i] = malloc(num_samples * sizeof(float));
+          r_im_cpu[i] = malloc(num_samples * sizeof(float));
+          r_re_gpu[i] = malloc(num_samples * sizeof(float));
+          r_im_gpu[i] = malloc(num_samples * sizeof(float));
+        }
+
+        void *d_tx_sig = NULL, *d_rx_sig = NULL, *d_channel_coeffs_gpu = NULL, *h_tx_sig_pinned = NULL;
+        const int num_samples_to_process = num_samples - chan_desc->channel_offset;
+        const int padding_len = channel_length - 1;
+        const size_t channel_buffer_size = nb_tx * nb_rx * channel_length * sizeof(float2);
+        const size_t tx_buffer_bytes = nb_tx * (num_samples_to_process + padding_len) * 2 * sizeof(float);
+        const size_t rx_buffer_bytes = nb_rx * num_samples_to_process * sizeof(float2);
+
+#if defined(USE_UNIFIED_MEMORY)
+        cudaMallocManaged(&d_tx_sig, tx_buffer_bytes, cudaMemAttachGlobal);
+        cudaMallocManaged(&d_rx_sig, rx_buffer_bytes, cudaMemAttachGlobal);
+        cudaMallocManaged(&d_channel_coeffs_gpu, channel_buffer_size, cudaMemAttachGlobal);
+        h_tx_sig_pinned = d_tx_sig;
+#elif defined(USE_ATS_MEMORY)
+        h_tx_sig_pinned = malloc(tx_buffer_bytes);
+        d_tx_sig = NULL;
+        cudaMalloc(&d_rx_sig, rx_buffer_bytes);
+        cudaMalloc(&d_channel_coeffs_gpu, channel_buffer_size);
+#else // EXPLICIT COPY
+        cudaMalloc(&d_tx_sig, tx_buffer_bytes);
+        cudaMalloc(&d_rx_sig, rx_buffer_bytes);
+        cudaMalloc(&d_channel_coeffs_gpu, channel_buffer_size);
+        cudaMallocHost(&h_tx_sig_pinned, tx_buffer_bytes);
+#endif
+
+        double total_cpu_ns = 0;
+        double total_gpu_ns = 0;
+
+        int num_links = chan_desc->nb_tx * chan_desc->nb_rx;
+        float *h_channel_coeffs = (float *)malloc(num_links * channel_length * sizeof(float2));
+        for (int link = 0; link < num_links; link++) {
+          for (int l = 0; l < channel_length; l++) {
+            int idx = link * channel_length + l;
+            ((float2 *)h_channel_coeffs)[idx].x = (float)chan_desc->ch[link][l].r;
+            ((float2 *)h_channel_coeffs)[idx].y = (float)chan_desc->ch[link][l].i;
+          }
+        }
+
+        struct timespec start, end;
+
+        generate_random_signal_interleaved(s_interleaved, nb_tx, num_samples);
+
+        const int padded_num_samples = num_samples + padding_len;
+        float *h_tx_ptr = (float *)h_tx_sig_pinned;
+        memset(h_tx_ptr, 0, tx_buffer_bytes);
+        for (int j = 0; j < nb_tx; j++) {
+          float *data_start_ptr = h_tx_ptr + (j * padded_num_samples + padding_len) * 2;
+          memcpy(data_start_ptr, s_interleaved[j], num_samples * 2 * sizeof(float));
+        }
+
+        float **tx_sig_for_cpu = malloc(nb_tx * sizeof(float *));
+        for (int j = 0; j < nb_tx; j++) {
+          tx_sig_for_cpu[j] = h_tx_ptr + (j * padded_num_samples + padding_len) * 2;
+        }
+
+        multipath_channel_float(chan_desc, tx_sig_for_cpu, r_re_cpu, r_im_cpu, num_samples, 1, 0);
+
+        clock_gettime(CLOCK_MONOTONIC, &start);
+        for (int t = 0; t < num_trials; t++) {
+          multipath_channel_float(chan_desc, tx_sig_for_cpu, r_re_cpu, r_im_cpu, num_samples, 1, 0);
+        }
+        clock_gettime(CLOCK_MONOTONIC, &end);
+        total_cpu_ns = (end.tv_sec - start.tv_sec) * 1e9 + (end.tv_nsec - start.tv_nsec);
+        free(tx_sig_for_cpu);
+
+        clock_gettime(CLOCK_MONOTONIC, &start);
+        for (int t = 0; t < num_trials; t++) {
+          multipath_channel_cuda(r_re_gpu,
+                                 r_im_gpu,
+                                 nb_tx,
+                                 nb_rx,
+                                 channel_length,
+                                 num_samples,
+                                 chan_desc->channel_offset,
+                                 h_channel_coeffs,
+                                 d_tx_sig,
+                                 d_rx_sig,
+                                 d_channel_coeffs_gpu,
+                                 h_tx_sig_pinned);
+        }
+        cudaDeviceSynchronize();
+        clock_gettime(CLOCK_MONOTONIC, &end);
+        total_gpu_ns = (end.tv_sec - start.tv_sec) * 1e9 + (end.tv_nsec - start.tv_nsec);
+
+        int verification_passed = (verify_results(r_re_cpu, r_im_cpu, r_re_gpu, r_im_gpu, nb_rx, num_samples) == 0);
+        double avg_cpu_us = (total_cpu_ns / num_trials) / 1000.0;
+        double avg_gpu_us = (total_gpu_ns / num_trials) / 1000.0;
+        double speedup = (avg_gpu_us > 0) ? (avg_cpu_us / avg_gpu_us) : 0;
+
+        printf("%-15s | %-15s | %-15d | %-15.2f | %-15.2f | %-15.2fx | %-10s\n",
+               channel_type_names[c],
+               mimo_str,
+               num_samples,
+               avg_cpu_us,
+               avg_gpu_us,
+               speedup,
+               (verification_passed ? "PASSED" : "FAILED"));
+
+        free(h_channel_coeffs);
+        for (int i = 0; i < nb_tx; i++) {
+          free(s_interleaved[i]);
+        }
+        for (int i = 0; i < nb_rx; i++) {
+          free(r_re_cpu[i]);
+          free(r_im_cpu[i]);
+          free(r_re_gpu[i]);
+          free(r_im_gpu[i]);
+        }
+        free(s_interleaved);
+        free(r_re_cpu);
+        free(r_im_cpu);
+        free(r_re_gpu);
+        free(r_im_gpu);
+#if defined(USE_UNIFIED_MEMORY)
+        cudaFree(d_tx_sig);
+#elif defined(USE_ATS_MEMORY)
+        free(h_tx_sig_pinned);
+#else // EXPLICIT COPY
+        cudaFree(d_tx_sig);
+        cudaFreeHost(h_tx_sig_pinned);
+#endif
+        cudaFree(d_rx_sig);
+        cudaFree(d_channel_coeffs_gpu);
+        free_manual_channel_desc(chan_desc);
+      }
+    }
+  }
+
+  printf(
+      "----------------------------------------------------------------------------------------------------------------------------"
+      "--------------\n");
+  printf("Benchmark finished.\n");
+
+  return 0;
+}

openair1/PHY/TOOLS/tests/test_noise.c

@@ -0,0 +1,186 @@
+/*
+ * Licensed to the OpenAirInterface (OAI) Software Alliance under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The OpenAirInterface Software Alliance licenses this file to You under
+ * the OAI Public License, Version 1.1  (the "License"); you may not use this file
+ * except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.openairinterface.org/?page_id=698
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *-------------------------------------------------------------------------------
+ * For more information about the OpenAirInterface (OAI) Software Alliance:
+ *      contact@openairinterface.org
+ */
+
+#include <stdio.h>
+#include <stdlib.h>
+#include <math.h>
+#include <string.h>
+#include <time.h>
+#include "PHY/TOOLS/tools_defs.h"
+#include "SIMULATION/TOOLS/sim.h"
+#include "SIMULATION/TOOLS/oai_cuda.h"
+#include "common/utils/LOG/log.h"
+#include "common/utils/utils.h"
+#include <cuda_runtime.h>
+
+configmodule_interface_t *uniqCfg = NULL;
+
+void exit_function(const char *file, const char *function, const int line, const char *s, const int assert_not_exit)
+{
+  fprintf(stderr, "Exit function called from %s:%d in %s(). Message: %s\n", file, line, function, s);
+  exit(1);
+}
+
+void generate_random_signal(float **sig_re, float **sig_im, int nb_ant, int num_samples)
+{
+  for (int i = 0; i < nb_ant; i++) {
+    for (int j = 0; j < num_samples; j++) {
+      sig_re[i][j] = (float)((rand() % 20000) - 10000);
+      sig_im[i][j] = (float)((rand() % 20000) - 10000);
+    }
+  }
+}
+
+int main(int argc, char **argv)
+{
+  logInit();
+  randominit(0);
+
+  int nb_rx_configs[] = {1, 2, 4, 8};
+  int num_samples_configs[] = {30720, 61440, 122880};
+  int num_trials = 100;
+  float snr_db = 10.0f;
+
+  printf("Starting Noise Generation Benchmark (CPU vs. CUDA)\n");
+  printf("Averaging each test case over %d trials.\n", num_trials);
+  printf("---------------------------------------------------------------------------------------------\n");
+  printf("%-15s | %-15s | %-15s | %-15s | %-15s\n", "Antennas", "Signal Length", "CPU Time (us)", "CUDA Time (us)", "Speedup");
+  printf("---------------------------------------------------------------------------------------------\n");
+
+  for (int r = 0; r < sizeof(nb_rx_configs) / sizeof(int); r++) {
+    for (int s = 0; s < sizeof(num_samples_configs) / sizeof(int); s++) {
+      int nb_rx = nb_rx_configs[r];
+      int num_samples = num_samples_configs[s];
+
+      float **r_re = malloc(nb_rx * sizeof(float *));
+      float **r_im = malloc(nb_rx * sizeof(float *));
+      for (int i = 0; i < nb_rx; i++) {
+        r_re[i] = malloc(num_samples * sizeof(float));
+        r_im[i] = malloc(num_samples * sizeof(float));
+      }
+
+      c16_t **output_cpu = malloc(nb_rx * sizeof(c16_t *));
+      c16_t **output_gpu = malloc(nb_rx * sizeof(c16_t *));
+      output_cpu[0] = malloc(nb_rx * num_samples * sizeof(c16_t));
+      output_gpu[0] = malloc(nb_rx * num_samples * sizeof(c16_t));
+      for (int i = 1; i < nb_rx; i++) {
+        output_cpu[i] = output_cpu[0] + i * num_samples;
+        output_gpu[i] = output_gpu[0] + i * num_samples;
+      }
+
+      void *d_r_sig, *d_output_sig, *d_curand_states;
+      void *h_r_sig_pinned, *h_output_sig_pinned;
+
+#if defined(USE_UNIFIED_MEMORY)
+      cudaMallocManaged(&d_r_sig, nb_rx * num_samples * sizeof(float) * 2, cudaMemAttachGlobal);
+      cudaMallocManaged(&d_output_sig, nb_rx * num_samples * sizeof(short) * 2, cudaMemAttachGlobal);
+
+      int deviceId;
+      cudaGetDevice(&deviceId);
+      cudaMemAdvise(d_r_sig, nb_rx * num_samples * sizeof(float) * 2, cudaMemAdviseSetReadMostly, deviceId);
+      cudaMemAdvise(d_output_sig, nb_rx * num_samples * sizeof(short) * 2, cudaMemAdviseSetPreferredLocation, deviceId);
+      cudaMemAdvise(d_output_sig, nb_rx * num_samples * sizeof(short) * 2, cudaMemAdviseSetAccessedBy, cudaCpuDeviceId);
+
+      // Pinned memory is not used in the UM path for this wrapper
+      h_r_sig_pinned = NULL;
+      h_output_sig_pinned = NULL;
+#else
+      cudaMalloc(&d_r_sig, nb_rx * num_samples * sizeof(float) * 2);
+      cudaMalloc(&d_output_sig, nb_rx * num_samples * sizeof(short) * 2);
+      cudaMallocHost(&h_r_sig_pinned, nb_rx * num_samples * sizeof(float) * 2);
+      cudaMallocHost(&h_output_sig_pinned, nb_rx * num_samples * sizeof(short) * 2);
+#endif
+      d_curand_states = create_and_init_curand_states_cuda(nb_rx * num_samples, time(NULL));
+
+      double total_cpu_ns = 0;
+      double total_gpu_ns = 0;
+
+      double ts = 1.0 / 30.72e6;
+      float signal_power = 1.0f;
+      float sigma2 = signal_power / powf(10.0f, snr_db / 10.0f);
+
+      for (int t = 0; t < num_trials; t++) {
+        generate_random_signal(r_re, r_im, nb_rx, num_samples);
+
+        struct timespec start, end;
+
+        clock_gettime(CLOCK_MONOTONIC, &start);
+        add_noise_float(output_cpu, (const float **)r_re, (const float **)r_im, sigma2, num_samples, 0, ts, 0, false, nb_rx);
+        clock_gettime(CLOCK_MONOTONIC, &end);
+        total_cpu_ns += (end.tv_sec - start.tv_sec) * 1e9 + (end.tv_nsec - start.tv_nsec);
+
+        clock_gettime(CLOCK_MONOTONIC, &start);
+        add_noise_cuda((const float **)r_re,
+                       (const float **)r_im,
+                       output_gpu,
+                       num_samples,
+                       nb_rx,
+                       sigma2,
+                       ts,
+                       0,
+                       0,
+                       0,
+                       0,
+                       d_r_sig,
+                       d_output_sig,
+                       d_curand_states,
+                       h_r_sig_pinned,
+                       h_output_sig_pinned);
+        cudaDeviceSynchronize();
+        clock_gettime(CLOCK_MONOTONIC, &end);
+        total_gpu_ns += (end.tv_sec - start.tv_sec) * 1e9 + (end.tv_nsec - start.tv_nsec);
+      }
+
+      double avg_cpu_us = (total_cpu_ns / num_trials) / 1000.0;
+      double avg_gpu_us = (total_gpu_ns / num_trials) / 1000.0;
+      double speedup = (avg_gpu_us > 0) ? (avg_cpu_us / avg_gpu_us) : 0;
+
+      printf("%-15d | %-15d | %-15.2f | %-15.2f | %-15.2fx\n", nb_rx, num_samples, avg_cpu_us, avg_gpu_us, speedup);
+
+      for (int i = 0; i < nb_rx; i++) {
+        free(r_re[i]);
+        free(r_im[i]);
+      }
+      free(r_re);
+      free(r_im);
+      free(output_cpu[0]);
+      free(output_gpu[0]);
+      free(output_cpu);
+      free(output_gpu);
+
+#if defined(USE_UNIFIED_MEMORY)
+      cudaFree(d_r_sig);
+      cudaFree(d_output_sig);
+#else
+      cudaFree(d_r_sig);
+      cudaFree(d_output_sig);
+      cudaFreeHost(h_r_sig_pinned);
+      cudaFreeHost(h_output_sig_pinned);
+#endif
+      destroy_curand_states_cuda(d_curand_states);
+    }
+  }
+
+  printf("---------------------------------------------------------------------------------------------\n");
+  printf("Benchmark finished.\n");
+
+  return 0;
+}

openair1/SIMULATION/CMakeLists.txt

@@ -1,3 +1,4 @@
 if(ENABLE_PHYSIM_TESTS)
   add_subdirectory(tests)
+  add_subdirectory(TOOLS)
 endif()

openair1/SIMULATION/NR_PHY/dlsim.c

@@ -90,6 +90,11 @@
 #define inMicroS(a) (((double)(a))/(get_cpu_freq_GHz()*1000.0))
 #include "SIMULATION/LTE_PHY/common_sim.h"
 
+#ifdef ENABLE_CUDA
+#include <cuda_runtime.h>
+#include "SIMULATION/TOOLS/oai_cuda.h"
+#endif
+
 const char *__asan_default_options()
 {
   /* don't do leak checking in nr_ulsim, not finished yet */
@@ -331,7 +336,7 @@ int main(int argc, char **argv)
   double eff_tp_check = 0.7;
   uint32_t TBS = 0;
   c16_t **txdata;
-  double **s_re,**s_im,**r_re,**r_im;
+  float **s_interleaved, **r_re, **r_im;
   //double iqim = 0.0;
   //unsigned char pbch_pdu[6];
   //  int sync_pos, sync_pos_slot;
@@ -408,8 +413,26 @@ int main(int argc, char **argv)
 
   FILE *scg_fd=NULL;
 
-  while ((c = getopt(argc, argv, "--:O:f:hA:p:f:g:i:n:s:S:t:v:x:y:z:o:H:M:N:F:GR:d:PI:L:a:b:e:m:w:T:U:q:X:Y:Z:")) != -1) {
+  int use_cuda = 0;
 
+  void *h_tx_sig_pinned = NULL;
+
+#ifdef ENABLE_CUDA
+  void *d_tx_sig = NULL, *d_intermediate_sig = NULL, *d_final_output = NULL;
+  void *d_curand_states = NULL;
+  void *h_final_output_pinned = NULL;
+  void *d_channel_coeffs_gpu = NULL;
+#endif
+
+  static struct option long_options[] = {{"cuda", no_argument, 0, 0}, {0, 0, 0, 0}};
+  int option_index = 0;
+
+  while ((c = getopt_long(argc,
+                          argv,
+                          "O:f:hA:p:g:i:n:s:S:t:v:x:y:z:o:H:M:N:F:GR:d:PI:L:a:b:e:m:w:T:U:q:X:Y:Z:",
+                          long_options,
+                          &option_index))
+         != -1) {
     /* ignore long options starting with '--', option '-O' and their arguments that are handled by configmodule */
     /* with this opstring getopt returns 1 for non-option arguments, refer to 'man 3 getopt' */
     if (c == 1 || c == '-' || c == 'O')
@@ -417,6 +440,14 @@ int main(int argc, char **argv)
 
     printf("handling optarg %c\n",c);
     switch (c) {
+    #ifdef ENABLE_CUDA
+    case 0:
+      if (strcmp(long_options[option_index].name, "cuda") == 0) {
+        use_cuda = 1;
+      }
+    #endif
+        break;
+
     case 'f':
       scg_fd = fopen(optarg,"r");
 
@@ -668,6 +699,31 @@ int main(int argc, char **argv)
   /* initialize the sin table */
   InitSinLUT();
 
+#ifdef ENABLE_CUDA
+  init_cuda_chsim_buffers(use_cuda,
+                          n_tx,
+                          n_rx,
+                          &d_tx_sig,
+                          &d_intermediate_sig,
+                          &d_final_output,
+                          &d_curand_states,
+                          &h_tx_sig_pinned,
+                          &h_final_output_pinned,
+                          &d_channel_coeffs_gpu);
+#endif
+
+#if !defined(ENABLE_CUDA) || !use_cuda
+  printf("Pre-allocating padded host memory for the CPU channel pipeline...\n");
+  int num_samples_alloc = 153600;
+  const int max_padding_alloc = 256 - 1;
+  size_t padded_tx_alloc_bytes = n_tx * (num_samples_alloc + max_padding_alloc) * 2 * sizeof(float);
+  h_tx_sig_pinned = malloc(padded_tx_alloc_bytes);
+  if (h_tx_sig_pinned == NULL) {
+    printf("Error: Failed to allocate host buffer for CPU path\n");
+    exit(-1);
+  }
+#endif
+
   get_softmodem_params()->phy_test = 1;
   get_softmodem_params()->do_ra = 0;
   IS_SOFTMODEM_DLSIM = true;
@@ -880,6 +936,11 @@ int main(int argc, char **argv)
                                 0,
                                 0);
 
+                                  float *h_channel_coeffs = NULL;
+  if (use_cuda) {
+    int num_links = n_tx * n_rx;
+    h_channel_coeffs = (float *)malloc(num_links * gNB2UE->channel_length * sizeof(float) * 2);
+  }
   if (gNB2UE==NULL) {
     printf("Problem generating channel model. Exiting.\n");
     exit(-1);
@@ -890,23 +951,20 @@ int main(int argc, char **argv)
   int slot_offset = get_samples_slot_timestamp(frame_parms, slot);
   int slot_length = slot_offset - get_samples_slot_timestamp(frame_parms, slot - 1);
 
-  s_re = malloc(n_tx*sizeof(double*));
-  s_im = malloc(n_tx*sizeof(double*));
-  r_re = malloc(n_rx*sizeof(double*));
-  r_im = malloc(n_rx*sizeof(double*));
-  txdata = malloc(n_tx*sizeof(int*));
+  s_interleaved = malloc(n_tx * sizeof(float *));
+  r_re = malloc(n_rx * sizeof(float *));
+  r_im = malloc(n_rx * sizeof(float *));
+  txdata = malloc(n_tx * sizeof(int *));
 
   for (i = 0; i < n_tx; i++) {
-    s_re[i] = calloc(1, slot_length * sizeof(double));
-    s_im[i] = calloc(1, slot_length * sizeof(double));
-
+    s_interleaved[i] = malloc(slot_length * 2 * sizeof(float));
     printf("Allocating %d samples for txdata\n", frame_length_complex_samples);
     txdata[i] = calloc(1, frame_length_complex_samples * sizeof(int));
   }
 
   for (i = 0; i < n_rx; i++) {
-    r_re[i] = calloc(1, slot_length * sizeof(double));
-    r_im[i] = calloc(1, slot_length * sizeof(double));
+    r_re[i] = calloc(1, slot_length * sizeof(float));
+    r_im[i] = calloc(1, slot_length * sizeof(float));
   }
 
   if (pbch_file_fd!=NULL) {
@@ -1004,8 +1062,7 @@ int main(int argc, char **argv)
     csv_file = fopen(filename_csv, "a");
     if (csv_file == NULL) {
       printf("Can't open file \"%s\", errno %d\n", filename_csv, errno);
-      free(s_re);
-      free(s_im);
+      free(s_interleaved);
       free(r_re);
       free(r_im);
       free(txdata);
@@ -1025,6 +1082,10 @@ int main(int argc, char **argv)
     exit(1);
   }
 
+  time_stats_t channel_stats = {0};
+  time_stats_t noise_stats = {0};
+  time_stats_t pipeline_stats = {0};
+
   for (SNR = snr0; SNR < snr1 && !stop; SNR += .2) {
 
     varArray_t *table_tx=initVarArray(1000,sizeof(double));
@@ -1179,10 +1240,10 @@ int main(int argc, char **argv)
         int symbol_length = frame_parms->ofdm_symbol_size + frame_parms->nb_prefix_samples;
         double txlev_sum = compute_tx_energy_level(txdata, n_tx, symbol_offset, symbol_length, n_trials);
 
-        for (i = 0; i < slot_length; i++) {
-          for (aa=0; aa<frame_parms->nb_antennas_tx; aa++) {
-            s_re[aa][i] = ((double)(((short *)&txdata[aa][slot_offset]))[(i << 1)]);
-            s_im[aa][i] = ((double)(((short *)&txdata[aa][slot_offset]))[(i << 1) + 1]);
+        for (int aa = 0; aa < frame_parms->nb_antennas_tx; aa++) {
+          for (int i = 0; i < slot_length; i++) {
+            s_interleaved[aa][2 * i] = (float)((short *)&txdata[aa][slot_offset])[2 * i];
+            s_interleaved[aa][2 * i + 1] = (float)((short *)&txdata[aa][slot_offset])[2 * i + 1];
           }
         }
 
@@ -1191,25 +1252,98 @@ int main(int argc, char **argv)
         // Estimate noise power from the transmitter level and SNR
         double sigma2 =
             compute_noise_variance(txlev_sum, UE->frame_parms.ofdm_symbol_size, pdsch_pdu_rel15->rbSize, 1, SNR, n_trials);
+        const int padding_len = gNB2UE->channel_length - 1;
+        const int padded_slot_length = slot_length + padding_len;
+        float *h_tx_ptr = (float *)h_tx_sig_pinned;
+        size_t total_padded_bytes_for_slot = n_tx * padded_slot_length * 2 * sizeof(float);
+        memset(h_tx_ptr, 0, total_padded_bytes_for_slot);
+
+        for (int j = 0; j < n_tx; j++) {
+          float *data_start_ptr = h_tx_ptr + (j * padded_slot_length + padding_len) * 2;
+          memcpy(data_start_ptr, s_interleaved[j], slot_length * 2 * sizeof(float));
+        }
 
         for (aa = 0; aa < n_rx; aa++) {
-          bzero(r_re[aa], slot_length * sizeof(double));
-          bzero(r_im[aa], slot_length * sizeof(double));
+          bzero(r_re[aa], slot_length * sizeof(float));
+          bzero(r_im[aa], slot_length * sizeof(float));
         }
 
         // Apply MIMO Channel
-        multipath_channel(gNB2UE, s_re, s_im, r_re, r_im, slot_length, 0, (n_trials == 1) ? 1 : 0);
-        add_noise(UE->common_vars.rxdata,
-                  (const double **)r_re,
-                  (const double **)r_im,
-                  sigma2,
-                  slot_length,
-                  slot_offset,
-                  ts,
-                  delay,
-                  pdu_bit_map,
-                  0x1,
-                  UE->frame_parms.nb_antennas_rx);
+#ifdef ENABLE_CUDA
+        if (use_cuda) {
+#if defined(USE_UNIFIED_MEMORY)
+          int deviceId;
+          cudaGetDevice(&deviceId);
+          cudaMemPrefetchAsync(d_tx_sig, n_tx * padded_slot_length * sizeof(float) * 2, deviceId, 0);
+#endif
+          start_meas(&pipeline_stats);
+          random_channel(gNB2UE, 0);
+          int num_links = gNB2UE->nb_tx * gNB2UE->nb_rx;
+          for (int link = 0; link < num_links; link++) {
+            for (int l = 0; l < gNB2UE->channel_length; l++) {
+              int idx = link * gNB2UE->channel_length + l;
+              ((float2 *)h_channel_coeffs)[idx].x = (float)gNB2UE->ch[link][l].r;
+              ((float2 *)h_channel_coeffs)[idx].y = (float)gNB2UE->ch[link][l].i;
+            }
+          }
+          run_channel_pipeline_cuda(UE->common_vars.rxdata,
+                                    n_tx,
+                                    n_rx,
+                                    gNB2UE->channel_length,
+                                    slot_length,
+                                    h_channel_coeffs,
+                                    (float)sigma2,
+                                    ts,
+                                    pdu_bit_map,
+                                    0x1,
+                                    slot_offset,
+                                    delay,
+                                    d_tx_sig,
+                                    d_intermediate_sig,
+                                    d_final_output,
+                                    d_curand_states,
+                                    h_tx_sig_pinned,
+                                    h_final_output_pinned,
+                                    d_channel_coeffs_gpu);
+          cudaDeviceSynchronize();
+          stop_meas(&pipeline_stats);
+        } else
+#endif
+        {
+          float **tx_sig_for_cpu = malloc(n_tx * sizeof(float *));
+          float *h_tx_ptr = (float *)h_tx_sig_pinned;
+          const int padding_len = gNB2UE->channel_length - 1;
+          const int padded_slot_length = slot_length + padding_len;
+
+          for (int j = 0; j < n_tx; j++) {
+            tx_sig_for_cpu[j] = h_tx_ptr + (j * padded_slot_length + padding_len) * 2;
+          }
+
+          for (aa = 0; aa < n_rx; aa++) {
+            bzero(r_re[aa], slot_length * sizeof(float));
+            bzero(r_im[aa], slot_length * sizeof(float));
+          }
+          start_meas(&channel_stats);
+          multipath_channel_float(gNB2UE, tx_sig_for_cpu, r_re, r_im, slot_length, 0, (n_trials == 1) ? 1 : 0);
+          stop_meas(&channel_stats);
+
+          free(tx_sig_for_cpu);
+
+          bool apply_phase_noise = (pdu_bit_map & 0x1);
+          start_meas(&noise_stats);
+          add_noise_float(UE->common_vars.rxdata,
+                          (const float **)r_re,
+                          (const float **)r_im,
+                          (float)sigma2,
+                          slot_length,
+                          slot_offset,
+                          ts,
+                          delay,
+                          apply_phase_noise,
+                          UE->frame_parms.nb_antennas_rx);
+          stop_meas(&noise_stats);
+        }
+
         dl_config.sfn = frame;
         dl_config.slot = slot;
         ue_dci_configuration(UE_mac, &dl_config, frame, slot);
@@ -1349,6 +1483,13 @@ int main(int argc, char **argv)
       if (gNB->phase_comp)
         printStatIndent2(&gNB->phase_comp_stats, "Phase Compensation");
 
+      if (use_cuda) {
+        printStatIndent(&pipeline_stats, "GPU Channel Pipeline");
+      } else {
+        printStatIndent(&channel_stats, "Multipath Channel (CPU)");
+        printStatIndent(&noise_stats, "Add Noise (CPU)");
+      }
+
       printf("\nUE function statistics (per %d us slot)\n", 1000 >> *scc->ssbSubcarrierSpacing);
       for (int i = RX_PDSCH_STATS; i <= DLSCH_PROCEDURES_STATS; i++) {
         printStatIndent(&UE->phy_cpu_stats.cpu_time_stats[i], UE->phy_cpu_stats.cpu_time_stats[i].meas_name);
@@ -1419,8 +1560,7 @@ int main(int argc, char **argv)
   free_channel_desc_scm(gNB2UE);
 
   for (i = 0; i < n_tx; i++) {
-    free(s_re[i]);
-    free(s_im[i]);
+    free(s_interleaved[i]);
     free(txdata[i]);
   }
   for (i = 0; i < n_rx; i++) {
@@ -1428,8 +1568,19 @@ int main(int argc, char **argv)
     free(r_im[i]);
   }
 
-  free(s_re);
-  free(s_im);
+#ifdef ENABLE_CUDA
+  free_cuda_chsim_buffers(use_cuda,
+                          &d_tx_sig,
+                          &d_intermediate_sig,
+                          &d_final_output,
+                          &d_curand_states,
+                          &h_tx_sig_pinned,
+                          &h_final_output_pinned,
+                          &h_channel_coeffs,
+                          &d_channel_coeffs_gpu);
+#endif
+
+  free(s_interleaved);
   free(r_re);
   free(r_im);
   free(txdata);

openair1/SIMULATION/NR_PHY/ulsim.c

@@ -90,6 +90,11 @@
 #include "time_meas.h"
 #include "utils.h"
 
+#ifdef ENABLE_CUDA
+#include <cuda_runtime.h>
+#include "SIMULATION/TOOLS/oai_cuda.h"
+#endif
+
 //#define DEBUG_ULSIM
 
 const char *__asan_default_options()
@@ -286,7 +291,7 @@ int main(int argc, char *argv[])
   int slot = 8, frame = 1;
   int do_SRS = 0;
   FILE *output_fd = NULL;
-  double **s_re,**s_im,**r_re,**r_im;
+  float **s_interleaved, **r_re, **r_im;
   //uint8_t write_output_file = 0;
   int trial, n_trials = 1, n_false_positive = 0, delay = 0;
   double maxDoppler = 0.0;
@@ -364,7 +369,25 @@ int main(int argc, char *argv[])
   int c;
   bool setAffinity=false;
   char gNBthreads[128]="n";
-  while ((c = getopt(argc, argv, "--:O:a:b:c:d:ef:g:h:i:jk:m:n:o::p:q:r:s:t:u:v:w:y:z:A:C:F:G:H:I:M:N:PR:S:T:U:L:ZW:E:X:Y:"))
+  static struct option long_options[] = {{"cuda", no_argument, 0, 0}, {0, 0, 0, 0}};
+  int option_index = 0;
+  int use_cuda = 0;
+
+  void *h_tx_sig_pinned = NULL;
+
+#ifdef ENABLE_CUDA
+  void *d_tx_sig = NULL, *d_intermediate_sig = NULL, *d_final_output = NULL;
+  void *d_curand_states = NULL;
+  void *h_final_output_pinned = NULL;
+  float *h_channel_coeffs = NULL;
+  void *d_channel_coeffs_gpu = NULL;
+#endif
+
+  while ((c = getopt_long(argc,
+                          argv,
+                          "--:O:a:b:c:d:ef:g:h:i:jk:m:n:o::p:q:r:s:t:u:v:w:y:z:A:C:F:G:H:I:M:N:PR:S:T:U:L:ZW:E:X:Y:",
+                          long_options,
+                          &option_index))
          != -1) {
     /* ignore long options starting with '--', option '-O' and their arguments that are handled by configmodule */
     /* with this opstring getopt returns 1 for non-option arguments, refer to 'man 3 getopt' */
@@ -373,7 +396,14 @@ int main(int argc, char *argv[])
 
     printf("handling optarg %c\n",c);
     switch (c) {
-
+#ifdef ENABLE_CUDA
+      case 0:
+        if (strcmp(long_options[option_index].name, "cuda") == 0) {
+          use_cuda = 1;
+          printf("CUDA acceleration enabled.\n");
+        }
+        break;
+#endif
     case 'a':
       start_symbol = atoi(optarg);
       AssertFatal(start_symbol >= 0 && start_symbol < 13,"start_symbol %d is not in 0..12\n",start_symbol);
@@ -748,10 +778,9 @@ int main(int argc, char *argv[])
   AssertFatal((gNB->if_inst = NR_IF_Module_init(0)) != NULL, "Cannot register interface");
   gNB->if_inst->NR_PHY_config_req = nr_phy_config_request;
 
-  s_re = malloc(n_tx*sizeof(double*));
-  s_im = malloc(n_tx*sizeof(double*));
-  r_re = malloc(n_rx*sizeof(double*));
-  r_im = malloc(n_rx*sizeof(double*));
+  s_interleaved = malloc(n_tx * sizeof(float *));
+  r_re = malloc(n_rx * sizeof(float *));
+  r_im = malloc(n_rx * sizeof(float *));
 
   NR_ServingCellConfigCommon_t *scc = calloc(1,sizeof(*scc));;
   prepare_scc(scc);
@@ -875,6 +904,35 @@ int main(int argc, char *argv[])
     exit(-1);
   }
 
+  const int num_samples_alloc = 153600;
+#ifdef ENABLE_CUDA
+  init_cuda_chsim_buffers(use_cuda,
+                          n_tx,
+                          n_rx,
+                          &d_tx_sig,
+                          &d_intermediate_sig,
+                          &d_final_output,
+                          &d_curand_states,
+                          &h_tx_sig_pinned,
+                          &h_final_output_pinned,
+                          &d_channel_coeffs_gpu);
+  if (use_cuda) {
+    int num_links = n_tx * n_rx;
+    h_channel_coeffs = (float *)malloc(num_links * UE2gNB->channel_length * sizeof(float2));
+  }
+#endif
+
+#if !defined(ENABLE_CUDA) || !use_cuda
+  printf("Pre-allocating padded host memory for the CPU channel pipeline...\n");
+  const int max_padding_alloc = 256 - 1;
+  size_t padded_tx_alloc_bytes = n_tx * (num_samples_alloc + max_padding_alloc) * 2 * sizeof(float);
+  h_tx_sig_pinned = malloc(padded_tx_alloc_bytes);
+  if (h_tx_sig_pinned == NULL) {
+    printf("Error: Failed to allocate host buffer for CPU path\n");
+    exit(-1);
+  }
+#endif
+
   // Configure UE
   UE = calloc(1, sizeof(PHY_VARS_NR_UE));
   PHY_vars_UE_g = malloc(sizeof(PHY_VARS_NR_UE**));
@@ -919,6 +977,10 @@ int main(int argc, char *argv[])
   nfapi_nr_ul_tti_request_t *UL_tti_req = &Sched_INFO->UL_tti_req;
   Sched_INFO->sched_response_id = -1;
 
+  time_stats_t channel_stats = {0};
+  time_stats_t noise_stats = {0};
+  time_stats_t pipeline_stats = {0};
+
   nr_phy_data_tx_t phy_data = {0};
 
   uint32_t errors_decoding = 0;
@@ -1047,14 +1109,13 @@ int main(int argc, char *argv[])
   printf("[ULSIM]: VALUE OF G: %u, TBS: %u\n", available_bits, TBS);
 
   int frame_length_complex_samples = gNB->frame_parms.samples_per_subframe * NR_NUMBER_OF_SUBFRAMES_PER_FRAME;
-  for (int aatx=0; aatx<n_tx; aatx++) {
-    s_re[aatx] = calloc(1,frame_length_complex_samples*sizeof(double));
-    s_im[aatx] = calloc(1,frame_length_complex_samples*sizeof(double));
+  for (int aatx = 0; aatx < n_tx; aatx++) {
+    s_interleaved[aatx] = calloc(1, frame_length_complex_samples * 2 * sizeof(float));
   }
 
-  for (int aarx=0; aarx<n_rx; aarx++) {
-    r_re[aarx] = calloc(1,frame_length_complex_samples*sizeof(double));
-    r_im[aarx] = calloc(1,frame_length_complex_samples*sizeof(double));
+  for (int aarx = 0; aarx < n_rx; aarx++) {
+    r_re[aarx] = calloc(1, frame_length_complex_samples * sizeof(float));
+    r_im[aarx] = calloc(1, frame_length_complex_samples * sizeof(float));
   }
 
   //for (int i=0;i<16;i++) printf("%f\n",gaussdouble(0.0,1.0));
@@ -1116,8 +1177,7 @@ int main(int argc, char *argv[])
     csv_file = fopen(filename_csv, "a");
     if (csv_file == NULL) {
       printf("Can't open file \"%s\", errno %d\n", filename_csv, errno);
-      free(s_re);
-      free(s_im);
+      free(s_interleaved);
       free(r_re);
       free(r_im);
       return 1;
@@ -1417,27 +1477,105 @@ int main(int argc, char *argv[])
           double sigma =
               compute_noise_variance(txlev_sum, gNB->frame_parms.ofdm_symbol_size, nb_rb, precod_nbr_layers, SNR, n_trials);
 
-          for (i = 0; i < slot_length; i++) {
-            for (int aa = 0; aa < UE->frame_parms.nb_antennas_tx; aa++) {
-              s_re[aa][i] = (double)UE->common_vars.txData[aa][slot_offset + i].r;
-              s_im[aa][i] = (double)UE->common_vars.txData[aa][slot_offset + i].i;
+          for (int aa = 0; aa < UE->frame_parms.nb_antennas_tx; aa++) {
+            for (i = 0; i < slot_length; i++) {
+              s_interleaved[aa][2 * i] = (float)UE->common_vars.txData[aa][slot_offset + i].r;
+              s_interleaved[aa][2 * i + 1] = (float)UE->common_vars.txData[aa][slot_offset + i].i;
             }
           }
 
-          multipath_channel(UE2gNB, s_re, s_im, r_re, r_im, slot_length, 0, (n_trials == 1) ? 1 : 0);
-          add_noise(rxdata,
-                    (const double **)r_re,
-                    (const double **)r_im,
-                    sigma,
-                    slot_length,
-                    slot_offset,
-                    ts,
-                    delay,
-                    pdu_bit_map,
-                    PUSCH_PDU_BITMAP_PUSCH_PTRS,
-                    gNB->frame_parms.nb_antennas_rx);
+          const int padding_len = UE2gNB->channel_length - 1;
+          const int padded_slot_length = slot_length + padding_len;
+          float *h_tx_ptr = (float *)h_tx_sig_pinned;
+          size_t total_padded_bytes_for_slot = n_tx * padded_slot_length * 2 * sizeof(float);
+          memset(h_tx_ptr, 0, total_padded_bytes_for_slot);
 
-        } /*End input_fd */
+          for (int j = 0; j < n_tx; j++) {
+            float *data_start_ptr = h_tx_ptr + (j * padded_slot_length + padding_len) * 2;
+            memcpy(data_start_ptr, s_interleaved[j], slot_length * 2 * sizeof(float));
+          }
+
+#ifdef ENABLE_CUDA
+          if (use_cuda) {
+#if defined(USE_UNIFIED_MEMORY)
+            int deviceId;
+            cudaGetDevice(&deviceId);
+            const int padding_len = UE2gNB->channel_length - 1;
+            const int padded_slot_length = slot_length + padding_len;
+            cudaMemPrefetchAsync(d_tx_sig, n_tx * padded_slot_length * 2 * sizeof(float), deviceId, 0);
+#endif
+
+            start_meas(&pipeline_stats);
+            random_channel(UE2gNB, 0);
+            int num_links = UE2gNB->nb_tx * UE2gNB->nb_rx;
+            if (h_channel_coeffs == NULL) {
+              h_channel_coeffs = (float *)malloc(num_links * 256 * sizeof(float2));
+            }
+
+            for (int link = 0; link < num_links; link++) {
+              for (int l = 0; l < UE2gNB->channel_length; l++) {
+                int idx = link * UE2gNB->channel_length + l;
+                ((float2 *)h_channel_coeffs)[idx].x = (float)UE2gNB->ch[link][l].r;
+                ((float2 *)h_channel_coeffs)[idx].y = (float)UE2gNB->ch[link][l].i;
+              }
+            }
+
+            run_channel_pipeline_cuda(rxdata,
+                                      n_tx,
+                                      n_rx,
+                                      UE2gNB->channel_length,
+                                      slot_length,
+                                      h_channel_coeffs,
+                                      (float)sigma,
+                                      ts,
+                                      pdu_bit_map,
+                                      PUSCH_PDU_BITMAP_PUSCH_PTRS,
+                                      slot_offset,
+                                      delay,
+                                      d_tx_sig,
+                                      d_intermediate_sig,
+                                      d_final_output,
+                                      d_curand_states,
+                                      h_tx_sig_pinned,
+                                      h_final_output_pinned,
+                                      d_channel_coeffs_gpu);
+            cudaDeviceSynchronize();
+            stop_meas(&pipeline_stats);
+
+          } else
+#endif
+          {
+            float **tx_sig_for_cpu = malloc(n_tx * sizeof(float *));
+            float *h_tx_ptr = (float *)h_tx_sig_pinned;
+            const int padding_len = UE2gNB->channel_length - 1;
+            const int padded_slot_length = slot_length + padding_len;
+
+            for (int j = 0; j < n_tx; j++) {
+              tx_sig_for_cpu[j] = h_tx_ptr + (j * padded_slot_length + padding_len) * 2;
+            }
+
+            start_meas(&channel_stats);
+            multipath_channel_float(UE2gNB, tx_sig_for_cpu, r_re, r_im, slot_length, 0, (n_trials == 1) ? 1 : 0);
+            stop_meas(&channel_stats);
+
+            free(tx_sig_for_cpu);
+
+            bool apply_phase_noise = (pdu_bit_map & PUSCH_PDU_BITMAP_PUSCH_PTRS);
+            start_meas(&noise_stats);
+            add_noise_float(rxdata,
+                            (const float **)r_re,
+                            (const float **)r_im,
+                            (float)sigma,
+                            slot_length,
+                            slot_offset,
+                            ts,
+                            delay,
+                            apply_phase_noise,
+                            gNB->frame_parms.nb_antennas_rx);
+            stop_meas(&noise_stats);
+          }
+        }
+        /*End input_fd */
 
         //----------------------------------------------------------
         //------------------- gNB phy procedures -------------------
@@ -1732,6 +1870,13 @@ int main(int argc, char *argv[])
       printStatIndent2(&gNB->srs_report_tlv_stats,"SRS report TLV build time");
       printStatIndent3(&gNB->srs_beam_report_stats,"SRS beam report build time");
       printStatIndent3(&gNB->srs_iq_matrix_stats,"SRS IQ matrix build time");
+
+      if (use_cuda) {
+        printStatIndent(&pipeline_stats, "GPU Channel Pipeline");
+      } else {
+        printStatIndent(&channel_stats, "Multipath Channel (CPU)");
+        printStatIndent(&noise_stats, "Add Noise (CPU)");
+      }
       printf("\n");
     }
 
@@ -1787,6 +1932,17 @@ int main(int argc, char *argv[])
     fclose(uci_ulsch_matlab_vec);
 
   free_and_zero(UE->phy_sim_test_buf);
+#ifdef ENABLE_CUDA
+  free_cuda_chsim_buffers(use_cuda,
+                          &d_tx_sig,
+                          &d_intermediate_sig,
+                          &d_final_output,
+                          &d_curand_states,
+                          &h_tx_sig_pinned,
+                          &h_final_output_pinned,
+                          &h_channel_coeffs,
+                          &d_channel_coeffs_gpu);
+#endif
 
   return ret;
 }

openair1/SIMULATION/TOOLS/CMakeLists.txt

@@ -0,0 +1,34 @@
+#/*
+# * Licensed to the OpenAirInterface (OAI) Software Alliance under one or more
+# * contributor license agreements.  See the NOTICE file distributed with
+# * this work for additional information regarding copyright ownership.
+# * The OpenAirInterface Software Alliance licenses this file to You under
+# * the OAI Public License, Version 1.1  (the "License"); you may not use this file
+# * except in compliance with the License.
+# * You may obtain a copy of the License at
+# *
+# *      http://www.openairinterface.org/?page_id=698
+# *
+# * Unless required by applicable law or agreed to in writing, software
+# * distributed under the License is distributed on an "AS IS" BASIS,
+# * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# * See the License for the specific language governing permissions and
+# * limitations under the License.
+# *-------------------------------------------------------------------------------
+# * For more information about the OpenAirInterface (OAI) Software Alliance:
+# *      contact@openairinterface.org
+# */
+
+if(CUDA_ENABLE)
+    add_library(oai_cuda_lib STATIC
+        multipath_channel.cu
+        phase_noise.cu
+        channel_pipeline.cu
+    )
+
+    target_link_libraries(oai_cuda_lib PUBLIC CUDA::toolkit)
+
+    target_include_directories(oai_cuda_lib PUBLIC
+        ${CMAKE_CURRENT_SOURCE_DIR}
+    )
+endif()

openair1/SIMULATION/TOOLS/DOC/gpu_acceleration.md

@@ -0,0 +1,226 @@
+# GPU-Accelerated Channel Simulation
+
+## 1\. Overview
+
+This document describes the CUDA-based GPU acceleration pipeline for the OAI channel simulation. The primary goal of this feature is to offload the computationally intensive `multipath_channel` convolution and `add_noise` functions from the CPU to the GPU. This overcomes existing performance bottlenecks and enables large-scale, real-time physical layer simulation that is not feasible with the CPU-based models.
+
+The result is a complete, high-performance pipeline integrated into the OAI simulators. The feature is supported by a new benchmark suite created to validate correctness and analyze performance across a wide range of channel models and hardware configurations.
+
+## 2\. Implementation
+
+This feature is implemented across a set of new CUDA source files and integrated into the existing OAI simulation framework.
+
+### 2.1 Multipath Channel Convolution (`multipath_channel.cu`)
+
+The core of the channel simulation is the multipath convolution. On the CPU, this is a sequential process where each output sample is calculated by iterating through all channel taps and accumulating the results. To parallelize this on the GPU, we assign one CUDA thread to calculate one output sample.
+
+The key to making this parallel approach efficient is the use of a tiled convolution pattern with `__shared__` memory. Instead of having every thread read from slow global memory for each step of the convolution, threads in a block cooperate to pre-fetch all the data they will need into the on-chip shared memory.
+
+To further optimize this, the responsibility for handling boundary conditions (the "halo" of past samples) has been moved from the GPU kernel to the host. The host code now prepares a larger input buffer that is **pre-padded** with `channel_length - 1` zeros before the actual signal data. This allows the kernel to be significantly simplified by removing the previous boundary-checking logic.
+
+```cpp
+// Snippet from the multipath_channel_kernel in multipath_channel.cu
+
+// Step 1: Each thread cooperates to load a tile of the pre-padded signal
+// into fast shared memory.
+const int padding_len = channel_length - 1;
+const int padded_num_samples = num_samples + padding_len;
+
+for (int k = tid; k < shared_mem_size; k += blockDim.x) {
+    int load_idx = block_start_idx + k;
+    // Unconditional load from the padded buffer
+    int interleaved_idx = 2 * (j * padded_num_samples + load_idx);
+    tx_shared[k] = make_float2(tx_sig[interleaved_idx], tx_sig[interleaved_idx + 1]);
+}
+__syncthreads();
+```
+
+Once the data is staged in fast shared memory, each thread can perform the full convolution for its assigned output sample. The inner loop iterates through the `channel_length` taps, and all reads of the input signal (`tx_sample`) come from the `tx_shared` buffer.
+
+```cpp
+// Snippet from multipath_channel_kernel in multipath_channel.cu
+// Step 2: Each thread performs convolution using the data in shared memory
+    for (int l = 0; l < channel_length; l++) {
+        // Read the historical transmit sample from the shared memory
+        float2 tx_sample = tx_shared[tid + (channel_length - 1) - l];
+        
+        // Get the corresponding channel weight from global memory
+        int chan_link_idx = ii + (j * nb_rx);
+        float2 chan_weight = d_channel_coeffs[chan_link_idx * channel_length + l];
+        
+        // Perform the complex multiply-accumulate
+        rx_tmp = complex_add(rx_tmp, complex_mul(tx_sample, chan_weight));
+    }
+    __syncthreads();
+}
+
+// Write the final result from the thread's private register to global memory...
+```
+
+### 2.2 Noise Generation (`phase_noise.cu`)
+
+After the multipath convolution, the next stage of the pipeline is to add realistic noise to the signal. This includes both Additive White Gaussian Noise (AWGN) and phase noise.
+
+The CPU implementation performs this in a simple loop. For each sample of the signal, it calls a standard C library function to generate pseudo-random numbers, scales them appropriately, and adds them to the sample. This process is sequential and computationally inexpensive for a single signal but becomes a bottleneck when simulating many channels that all require high-quality random numbers simultaneously.
+
+The GPU port follows the same parallelization strategy as the multipath kernel: one CUDA thread is assigned to process one single sample. The main challenge is generating millions of high-quality random numbers in parallel. To achieve this, the implementation relies on NVIDIA's **`cuRAND`** library.
+
+The `cuRAND` library is stateful, meaning each thread needs its own unique generator "state" to produce an independent sequence of random numbers. In our implementation, a pool of `curandState_t` objects is initialized once in global memory. When the noise kernel is launched, each thread is assigned a unique state from this pool. The thread uses its state to generate random numbers, and then writes the updated state back to global memory for the next use.
+
+A simplified view of this logic within the `add_noise_and_phase_noise_kernel` is shown below:
+
+```cpp
+// Simplified snippet from add_noise_and_phase_noise_kernel in phase_noise.cu
+__global__ void add_noise_and_phase_noise_kernel(...)
+{
+    // Each thread calculates its unique sample index 'i' and antenna index 'ii'...
+
+    // Load this thread's unique random number generator state
+    curandState_t local_state = states[ii * num_samples + i];
+    
+    // Generate random numbers using the local state
+    // Generates two normal-distributed numbers for AWGN (real and imaginary)
+    float2 awgn = curand_normal2(&local_state);
+    //    Generates one normal-distributed number for phase noise
+    float phase_error = curand_normal(&local_state) * pn_std_dev;
+
+    // Apply noise to the input signal from the previous kernel stage, and then aplu phase_error
+
+    // Write the updated state back to global memory for the next simulation run
+    states[ii * num_samples + i]= local_state; 
+
+    // Clamp the final floating-point value and store it as a short integer
+    output_sig[ii * num_samples + i]= make_short2(...);
+}
+```
+
+### 2.3 Pipeline Orchestration (`channel_pipeline.cu`)
+
+The `channel_pipeline.cu` file serves two primary functions: providing an interoperable API and orchestrating kernel execution.
+
+Its first purpose is to act as an abstraction layer between the host C/C++ code of the OAI simulator and the CUDA C++ device code. It exposes a C-style Application Programming Interface (API) using `extern "C"`, which allows the standard C-based components to call the GPU pipeline without needing to handle CUDA-specific syntax or complexities.
+
+Its second purpose is to orchestrate the execution of the individual CUDA kernels. It defines the logical flow of the channel simulation by launching the `multipath_channel_kernel` and `add_noise_and_phase_noise_kernel` in sequence and managing the data dependencies between them.
+
+The functions within this file, such as `run_channel_pipeline_cuda`, implement a consistent workflow. Upon being called, they first cast the generic `void*` pointers received from the C host into their specific CUDA data types. The implementation then contains the conditional logic, via preprocessor directives, to support the various memory models. Following data setup, the functions define the CUDA execution grid and block dimensions and launch the two kernels sequentially. The output of the multipath kernel is directed to an intermediate buffer, which serves as the input for the subsequent noise kernel. Finally, the functions manage synchronization with the host and handle the transfer of the final results from the GPU back to host memory.
+
+*The external API of these pipeline functions has been streamlined. The function signatures no longer require a separate `tx_sig_interleaved` parameter, instead relying on a single, pre-padded host buffer (`h_tx_sig_pinned`) as the authoritative source for the input signal, which simplifies the data flow from the simulators.*
+
+---
+
+## 3\. Memory Management Models
+
+To provide flexibility and enable performance testing on different hardware architectures, the project now supports three distinct memory management models. The desired model is selected at build time by passing a flag to the CMake build command.
+
+### 3.1 ATS Hybrid Model (`-DUSE_ATS_MEMORY=ON`)
+
+This is a hybrid approach that leverages Address Translation Services (ATS), a hardware feature allowing the GPU to directly access host-allocated memory. In our implementation, the large input signal buffer is allocated in host memory using `malloc`. The GPU kernel is then able to read this data directly from the host, eliminating the need for an initial Host-to-Device `cudaMemcpy` for that buffer. Intermediate and output buffers are still allocated on the device, and the final result is copied back to the host explicitly. Due to its better performance compared to the other methods, it's also the default behaviour when no flag is specified.
+
+### 3.2 Unified Memory Model (`-DUSE_UNIFIED_MEMORY=ON`)
+
+This model simplifies memory management by creating a single, managed memory space that is accessible to both the CPU and the GPU. Memory is allocated once using `cudaMallocManaged`. The CUDA runtime and driver then automatically handle the migration of data to whichever processor is accessing it (e.g., moving data to the GPU's memory when a kernel is launched). This removes the need for most explicit `cudaMemcpy` calls. While the CUDA driver automatically migrates data on-demand, the implementation includes further optimizations to guide this process for better performance. `cudaMemAdvise` is used to provide performance hints to the driver, such as setting a buffer's preferred location to the GPU or marking it as mostly read-only. Additionally, `cudaMemPrefetchAsync` is also used to explicitly move data to the GPU's memory ahead of time. This ensures the data is already local when a kernel begins execution, which helps to hide the latency of the data migration.
+
+### 3.3 Explicit Copy Model
+
+This is the traditional CUDA programming model. The host and device have separate and distinct memory spaces. The programmer is responsible for all data management. Memory must be allocated on the device using `cudaMalloc`, and data must be manually transferred between the host and device using explicit `cudaMemcpy` calls before and after a kernel launch. This configuration can be run with other flags turned off by using `-DUSE_ATS_MEMORY=OFF` and `-DUSE_UNIFIED_MEMORY=OFF`.
+
+-----
+
+## 4. Project Integration
+
+To ensure the feature is modular, the integration into the OAI project was handled at both the build system and source code levels. All of the new CUDA source files (`channel_pipeline.cu`, `multipath_channel.cu`, `phase_noise.cu`) are compiled into a single static library named `oai_cuda_lib`. This is defined in the main `CMakeLists.txt` file and is controlled by the `CUDA_ENABLE` CMake option, which is disabled by default. When this option is activated (`-DCUDA_ENABLE=ON`), the build system compiles the CUDA library and also defines a global `ENABLE_CUDA` preprocessor macro that is visible to the rest of the project.
+
+This `ENABLE_CUDA` macro is then used within the simulator files, such as `nr_dlsim.c` and `nr_ulsim.c`, to conditionally compile all CUDA-related code. This approach allows for the inclusion of the `oai_cuda.h` header, the addition of the `--cuda` runtime flag, and the pre-allocation of GPU memory at startup, all without affecting the standard CPU-only build.
+
+Inside the main processing loop, an `if (use_cuda)` statement acts as the primary runtime switch. To enable a direct comparison, timing measurement calls have been added to both the GPU and CPU execution paths. Also, both paths now source their data from the same common, pre-padded host buffer. The GPU path receives a pointer to the entire padded buffer and leverages its optimized kernel, while the CPU path uses a temporary array of offset pointers to work on the same data without being aware of the padding. This ensures both computations operate on the exact same source data, isolating the performance measurement to the channel processing itself. To execute the simulation on the GPU, the `--cuda` flag must be provided at runtime; otherwise, the program defaults to this refined CPU implementation.
+
+Finally, the build system links the `nr_dlsim` and `nr_ulsim` executables against the `oai_cuda_lib` only when the feature is enabled, creating a clean separation between the two codebases.
+
+---
+
+## 5\. Benchmark and Analysis Suite
+
+To validate the correctness, measure performance, and analyze the scalability of the GPU pipeline, a dedicated suite of tests was developed. The source code for these benchmarks is located in the `openair1/PHY/TOOLS/tests/` directory. This suite includes both focused unitary tests for individual components and a comprehensive benchmark for the end-to-end pipeline, which is the primary tool for performance evaluation.
+
+### 5.1 `test_channel_scalability.c`
+
+This program is the main tool for evaluating the end-to-end GPU channel simulation pipeline. It's designed to be highly configurable, allowing for the simulation of a wide variety of workloads and execution strategies.
+
+#### 5.1.1 Configuration and Usage
+
+The benchmark is configured at runtime using a set of command-line flags.
+
+| Flag | Long Version | Argument | Description |
+| :--- | :--- | :--- | :--- |
+| `-c` | `--num-channels` | `<N>` | Sets the number of independent channels to simulate. |
+| `-t` | `--nb-tx` | `<N>` | Sets the number of transmit antennas. |
+| `-r` | `--nb-rx` | `<N>` | Sets the number of receive antennas. |
+| `-s` | `--num-samples` | `<N>` | Sets the number of samples per signal. |
+| `-l` | `--ch-len` | `<N>` | Sets the length of the channel impulse response. |
+| `-n` | `--trials` | `<N>` | Sets the number of times each test is run for averaging. |
+| `-S` | `--sum-outputs` | (none) | Enables interference simulation by summing channel outputs. (Using unique input signals) |
+| `-m` | `--mode` | `<mode>` | Sets the GPU execution mode: `serial`, `stream`, or `batch`. |
+| `-h` | `--help` | (none) | Displays the help message with all options. |
+
+#### 5.1.2 Execution Modes
+
+The `-m` or `--mode` flag controls the strategy used to execute the pipeline for multiple channels:
+
+  * **`serial`**: This mode processes each channel sequentially. It loops through all channels and makes a synchronous call to `run_channel_pipeline_cuda` for each one, waiting for it to complete before starting the next. 
+  * **`stream`**: This mode is designed to test concurrency. It launches all channel simulations asynchronously into separate CUDA streams by calling `run_channel_pipeline_cuda_streamed` in a loop. This allows the GPU hardware to overlap the execution of many kernels. 
+  * **`batch`**: It aggregates the data for all channels on the host and makes a single call to `run_channel_pipeline_cuda_batched`. This launches one massive, unified kernel to process all channels at once, minimizing CPU overhead and maximizing GPU throughput. 
+
+#### 5.1.3 Automated Analysis Script (`run_scalability_analysis.sh`)
+
+To automate performance analysis, the `run_scalability_analysis.sh` script provides a convenient wrapper for the benchmark. It passes all command-line arguments directly to the `test_channel_scalability` executable while concurrently running `nvidia-smi` in the background to monitor the GPU's status, including utilization, power draw, and temperature. Upon completion, the script parses both the benchmark results and the GPU metrics to produce a single, consolidated report that correlates the pipeline's performance with the hardware's state during the test.
+
+### 5.2 `test_channel_simulation.c`
+
+The `test_channel_simulation.c` program was one of the initial benchmarks created for this project. Its primary function is to perform a direct performance comparison between the full, sequential CPU-based channel pipeline and the original synchronous GPU-based pipeline implemented in `run_channel_pipeline_cuda`. It served as an early-stage tool to validate the end-to-end functionality of the basic GPU port and to obtain initial speedup measurements before more advanced optimizations were developed.
+
+### 5.3 `test_multipath.c`
+
+The `test_multipath.c` program is a unitary test designed specifically to validate the `multipath_channel_cuda` function in isolation. The key feature of this test is the `verify_results` function, which performs a sample-by-sample comparison of the output arrays from both versions and calculates the Mean Squared Error (MSE). If the error is below a predefined tolerance, the test is marked as *"PASSED"* in the final report, confirming that the GPU kernel is producing numerically correct results.
+
+### 5.4 `test_noise.c`
+
+Similar to the multipath test, `test_noise.c` is a unitary test that focuses on a single component: the `add_noise_cuda` function. Its purpose is to validate the correctness of the GPU-based noise generation and measure its performance against the equivalent CPU version.
+
+### 5.5 `test_SSE.c`
+
+The `test_SSE.c` program is a CPU-focused benchmark designed to measure the performance impact of using Streaming SIMD Extensions (SSE) for the channel simulation. Its purpose is to compare the runtime of the standard, portable C implementation against a version that has been optimized with low-level SIMD intrinsics to perform parallel operations on the CPU.
+
+To facilitate this comparison, the `CMakeLists.txt` file compiles the same `test_SSE.c` source file into two separate executables: `test_cpu_std` and `test_cpu_sse`. The `test_cpu_sse` version is compiled with the `CHANNEL_SSE` flag, which activates the optimized code paths within the channel simulation functions. The `test_cpu_std` version is compiled without this flag and therefore runs the standard C code. By running both executables with the same input and comparing their execution times, the performance gain from the SSE optimization can be measured.
+
+-----
+
+### **6\. Direct CPU vs. GPU Speedup**
+
+To provide a clear, baseline performance comparison, the `test_channel_simulation` benchmark was run. This tool directly compares the execution time of the sequential, `float`-based CPU pipeline against the baseline synchronous GPU pipeline (`run_channel_pipeline_cuda`). The following tests were executed on a GH200 server using the ATS memory model. The results demonstrate the performance gains achieved by offloading the channel simulation to the GPU.
+
+| Channel Type | MIMO Config | Signal Length | CPU Pipeline (µs) | GPU Pipeline (µs) | Overall Speedup |
+| :--- | :--- | :--- | :--- | :--- | :--- |
+Short Channel   | 1x1             | 30720           | 654.12               | 45.60                | 14.34          x
+Short Channel   | 2x2             | 30720           | 2145.07              | 57.48                | 37.32          x
+Short Channel   | 4x4             | 30720           | 7611.24              | 84.20                | 90.40          x
+Short Channel   | 8x8             | 30720           | 28190.40             | 148.07               | 190.39         x
+Short Channel   | 1x1             | 61440           | 1305.85              | 54.16                | 24.11          x
+Short Channel   | 2x2             | 61440           | 4290.31              | 80.34                | 53.40          x
+Short Channel   | 4x4             | 61440           | 15264.63             | 136.62               | 111.73         x
+Short Channel   | 8x8             | 61440           | 56561.05             | 255.82               | 221.10         x
+Short Channel   | 1x1             | 122880          | 2617.06              | 79.33                | 32.99          x
+Short Channel   | 2x2             | 122880          | 8585.16              | 129.69               | 66.20          x
+Short Channel   | 4x4             | 122880          | 30543.07             | 233.36               | 130.88         x
+Short Channel   | 8x8             | 122880          | 113185.64            | 468.10               | 241.80         x
+Long Channel    | 1x1             | 30720           | 1068.25              | 44.37                | 24.08          x
+Long Channel    | 2x2             | 30720           | 3795.86              | 58.86                | 64.49          x
+Long Channel    | 4x4             | 30720           | 14209.40             | 89.08                | 159.51         x
+Long Channel    | 8x8             | 30720           | 54579.90             | 164.72               | 331.36         x
+Long Channel    | 1x1             | 61440           | 2139.73              | 56.02                | 38.20          x
+Long Channel    | 2x2             | 61440           | 7608.75              | 83.18                | 91.47          x
+Long Channel    | 4x4             | 61440           | 28463.66             | 145.80               | 195.23         x
+Long Channel    | 8x8             | 61440           | 109785.85            | 289.96               | 378.62         x
+Long Channel    | 1x1             | 122880          | 4267.25              | 80.65                | 52.91          x
+Long Channel    | 2x2             | 122880          | 15209.56             | 135.12               | 112.56         x
+Long Channel    | 4x4             | 122880          | 56951.08             | 249.47               | 228.29         x
+Long Channel    | 8x8             | 122880          | 222368.44            | 524.97               | 423.59         x

openair1/SIMULATION/TOOLS/channel_pipeline.cu

@@ -0,0 +1,443 @@
+/*
+ * Licensed to the OpenAirInterface (OAI) Software Alliance under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The OpenAirInterface Software Alliance licenses this file to You under
+ * the OAI Public License, Version 1.1  (the "License"); you may not use this file
+ * except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.openairinterface.org/?page_id=698
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *-------------------------------------------------------------------------------
+ * For more information about the OpenAirInterface (OAI) Software Alliance:
+ *      contact@openairinterface.org
+ */
+
+#include <stdio.h>
+#include <cuda_runtime.h>
+#include "oai_cuda.h"
+
+__global__ void multipath_channel_kernel_batched(const float2 *__restrict__ d_channel_coeffs,
+                                                 const float2 *__restrict__ tx_sig,
+                                                 float2 *__restrict__ rx_sig,
+                                                 int num_samples,
+                                                 int channel_length,
+                                                 int nb_tx,
+                                                 int nb_rx);
+
+__global__ void add_noise_and_phase_noise_kernel_batched(const float2 *__restrict__ r_sig,
+                                                         short2 *__restrict__ output_sig,
+                                                         curandState_t *states,
+                                                         int num_samples,
+                                                         int nb_rx,
+                                                         float sigma,
+                                                         float pn_std_dev,
+                                                         bool apply_phase_noise);
+
+#define CHECK_CUDA(val) checkCuda((val), #val, __FILE__, __LINE__)
+static void checkCuda(cudaError_t result, const char *const func, const char *const file, const int line)
+{
+  if (result != cudaSuccess) {
+    fprintf(stderr,
+            "CUDA Error at %s:%d code=%d(%s) \"%s\" \n",
+            file,
+            line,
+            static_cast<unsigned int>(result),
+            cudaGetErrorString(result),
+            func);
+    cudaDeviceReset();
+    exit(EXIT_FAILURE);
+  }
+}
+
+__global__ void sum_outputs_kernel(const short2 *__restrict__ *__restrict__ individual_outputs,
+                                   short2 *__restrict__ final_summed_output,
+                                   int num_channels,
+                                   int num_samples_per_antenna)
+{
+  const int i = blockIdx.x * blockDim.x + threadIdx.x;
+  if (i >= num_samples_per_antenna)
+    return;
+
+  float2 sum = make_float2(0.0f, 0.0f);
+
+  for (int c = 0; c < num_channels; c++) {
+    sum.x += individual_outputs[c][i].x;
+    sum.y += individual_outputs[c][i].y;
+  }
+
+  final_summed_output[i].x = (short)fmaxf(-32768.0f, fminf(32767.0f, sum.x));
+  final_summed_output[i].y = (short)fmaxf(-32768.0f, fminf(32767.0f, sum.y));
+}
+
+extern "C" {
+
+void init_cuda_chsim_buffers(int use_cuda,
+                             int n_tx,
+                             int n_rx,
+                             void **d_tx_sig,
+                             void **d_intermediate_sig,
+                             void **d_final_output,
+                             void **d_curand_states,
+                             void **h_tx_sig_pinned,
+                             void **h_final_output_pinned,
+                             void **d_channel_coeffs_gpu)
+{
+  if (use_cuda) {
+    int num_samples_alloc = 153600;
+
+#if defined(USE_UNIFIED_MEMORY)
+    printf("Allocating CUDA Unified Memory...\n");
+    const int max_padding_alloc = 256 - 1;
+    size_t padded_tx_alloc_bytes = n_tx * (num_samples_alloc + max_padding_alloc) * 2 * sizeof(float);
+    CHECK_CUDA(cudaMallocManaged(d_tx_sig, padded_tx_alloc_bytes, cudaMemAttachGlobal));
+    CHECK_CUDA(cudaMallocManaged(d_intermediate_sig, n_rx * num_samples_alloc * sizeof(float) * 2, cudaMemAttachGlobal));
+    CHECK_CUDA(cudaMallocManaged(d_final_output, n_rx * num_samples_alloc * sizeof(short) * 2, cudaMemAttachGlobal));
+    *h_tx_sig_pinned = *d_tx_sig;
+    *h_final_output_pinned = *d_final_output;
+    int deviceId;
+    CHECK_CUDA(cudaGetDevice(&deviceId));
+    CHECK_CUDA(cudaMemAdvise(*d_tx_sig, padded_tx_alloc_bytes, cudaMemAdviseSetReadMostly, deviceId));
+    CHECK_CUDA(cudaMemAdvise(*d_intermediate_sig,
+                             n_rx * num_samples_alloc * sizeof(float) * 2,
+                             cudaMemAdviseSetPreferredLocation,
+                             deviceId));
+    CHECK_CUDA(
+        cudaMemAdvise(*d_final_output, n_rx * num_samples_alloc * sizeof(short) * 2, cudaMemAdviseSetPreferredLocation, deviceId));
+    CHECK_CUDA(
+        cudaMemAdvise(*d_final_output, n_rx * num_samples_alloc * sizeof(short) * 2, cudaMemAdviseSetAccessedBy, cudaCpuDeviceId));
+
+#elif defined(USE_ATS_MEMORY)
+    printf("Allocating memory for ATS Hybrid path...\n");
+    const int max_padding_alloc = 256 - 1;
+    size_t padded_tx_alloc_bytes = n_tx * (num_samples_alloc + max_padding_alloc) * 2 * sizeof(float);
+    *h_tx_sig_pinned = malloc(padded_tx_alloc_bytes);
+    CHECK_CUDA(cudaMalloc(d_intermediate_sig, n_rx * num_samples_alloc * sizeof(float) * 2));
+    CHECK_CUDA(cudaMalloc(d_final_output, n_rx * num_samples_alloc * sizeof(short) * 2));
+    *h_final_output_pinned = malloc(n_rx * num_samples_alloc * sizeof(short) * 2);
+    *d_tx_sig = NULL;
+#else // Explicit copy method
+    printf("Pre-allocating GPU & Pinned memory for the channel pipeline...\n");
+    const int max_padding_alloc = 256 - 1;
+    size_t padded_tx_alloc_bytes = n_tx * (num_samples_alloc + max_padding_alloc) * 2 * sizeof(float);
+    CHECK_CUDA(cudaMalloc(d_tx_sig, padded_tx_alloc_bytes));
+    CHECK_CUDA(cudaMalloc(d_intermediate_sig, n_rx * num_samples_alloc * sizeof(float) * 2));
+    CHECK_CUDA(cudaMalloc(d_final_output, n_rx * num_samples_alloc * sizeof(short) * 2));
+    CHECK_CUDA(cudaMallocHost(h_tx_sig_pinned, padded_tx_alloc_bytes));
+    CHECK_CUDA(cudaMallocHost(h_final_output_pinned, n_rx * num_samples_alloc * sizeof(short) * 2));
+#endif
+    const int max_taps_alloc = 256; // Upper bound for channel length
+    size_t channel_buffer_size = n_tx * n_rx * max_taps_alloc * sizeof(float) * 2;
+    CHECK_CUDA(cudaMalloc(d_channel_coeffs_gpu, channel_buffer_size));
+    int num_rand_elements = n_rx * num_samples_alloc;
+    *d_curand_states = create_and_init_curand_states_cuda(num_rand_elements, time(NULL));
+  }
+}
+
+void free_cuda_chsim_buffers(int use_cuda,
+                             void **d_tx_sig_p,
+                             void **d_intermediate_sig_p,
+                             void **d_final_output_p,
+                             void **d_curand_states_p,
+                             void **h_tx_sig_pinned_p,
+                             void **h_final_output_pinned_p,
+                             float **h_channel_coeffs_p,
+                             void **d_channel_coeffs_gpu_p)
+{
+  if (use_cuda) {
+    printf("Freeing GPU and Pinned memory...\n");
+#if defined(USE_UNIFIED_MEMORY)
+    if (*d_tx_sig_p) {
+      cudaFree(*d_tx_sig_p);
+      *d_tx_sig_p = NULL;
+      *h_tx_sig_pinned_p = NULL;
+    }
+    if (*d_intermediate_sig_p) {
+      cudaFree(*d_intermediate_sig_p);
+      *d_intermediate_sig_p = NULL;
+    }
+    if (*d_final_output_p) {
+      cudaFree(*d_final_output_p);
+      *d_final_output_p = NULL;
+      *h_final_output_pinned_p = NULL;
+    }
+#elif defined(USE_ATS_MEMORY)
+    if (*h_tx_sig_pinned_p) {
+      free(*h_tx_sig_pinned_p);
+      *h_tx_sig_pinned_p = NULL;
+    }
+    if (*d_intermediate_sig_p) {
+      cudaFree(*d_intermediate_sig_p);
+      *d_intermediate_sig_p = NULL;
+    }
+    if (*d_final_output_p) {
+      cudaFree(*d_final_output_p);
+      *d_final_output_p = NULL;
+    }
+    if (*h_final_output_pinned_p) {
+      free(*h_final_output_pinned_p);
+      *h_final_output_pinned_p = NULL;
+    }
+#else
+    if (*d_tx_sig_p) {
+      cudaFree(*d_tx_sig_p);
+      *d_tx_sig_p = NULL;
+    }
+    if (*d_intermediate_sig_p) {
+      cudaFree(*d_intermediate_sig_p);
+      *d_intermediate_sig_p = NULL;
+    }
+    if (*d_final_output_p) {
+      cudaFree(*d_final_output_p);
+      *d_final_output_p = NULL;
+    }
+    if (*h_tx_sig_pinned_p) {
+      cudaFreeHost(*h_tx_sig_pinned_p);
+      *h_tx_sig_pinned_p = NULL;
+    }
+    if (*h_final_output_pinned_p) {
+      cudaFreeHost(*h_final_output_pinned_p);
+      *h_final_output_pinned_p = NULL;
+    }
+#endif
+    if (*d_channel_coeffs_gpu_p) {
+      cudaFree(*d_channel_coeffs_gpu_p);
+      *d_channel_coeffs_gpu_p = NULL;
+    }
+    destroy_curand_states_cuda(*d_curand_states_p);
+    *d_curand_states_p = NULL;
+    if (*h_channel_coeffs_p) {
+      free(*h_channel_coeffs_p);
+      *h_channel_coeffs_p = NULL;
+    }
+  } else {
+    if (*h_tx_sig_pinned_p) {
+      free(*h_tx_sig_pinned_p);
+      *h_tx_sig_pinned_p = NULL;
+    }
+  }
+}
+
+void run_channel_pipeline_cuda(c16_t **output_signal,
+                               int nb_tx,
+                               int nb_rx,
+                               int channel_length,
+                               uint32_t num_samples,
+                               float *h_channel_coeffs,
+                               float sigma2,
+                               double ts,
+                               uint16_t pdu_bit_map,
+                               uint16_t ptrs_bit_map,
+                               int slot_offset,
+                               int delay,
+                               void *d_tx_sig_void,
+                               void *d_intermediate_sig_void,
+                               void *d_final_output_void,
+                               void *d_curand_states_void,
+                               void *h_tx_sig_pinned_void,
+                               void *h_final_output_pinned_void,
+                               void *d_channel_coeffs_void)
+{
+  // --- Cast void pointers ---
+  float2 *d_intermediate_sig = (float2 *)d_intermediate_sig_void;
+  short2 *d_final_output = (short2 *)d_final_output_void;
+  curandState_t *d_curand_states = (curandState_t *)d_curand_states_void;
+  float2 *d_channel_coeffs = (float2 *)d_channel_coeffs_void;
+
+  const int padding_len = channel_length - 1;
+  const size_t padded_stride_bytes = (num_samples + padding_len) * 2 * sizeof(float);
+  const size_t total_padded_tx_bytes = nb_tx * padded_stride_bytes;
+
+  float *kernel_input_ptr;
+#if defined(USE_UNIFIED_MEMORY) || defined(USE_ATS_MEMORY)
+  kernel_input_ptr = (float *)h_tx_sig_pinned_void;
+#else
+  float *d_tx_sig = (float *)d_tx_sig_void;
+  float *h_tx_sig_pinned = (float *)h_tx_sig_pinned_void;
+  CHECK_CUDA(cudaMemcpy(d_tx_sig, h_tx_sig_pinned, total_padded_tx_bytes, cudaMemcpyHostToDevice));
+
+  kernel_input_ptr = d_tx_sig;
+#endif
+
+  size_t channel_size_bytes = nb_tx * nb_rx * channel_length * sizeof(float2);
+  CHECK_CUDA(cudaMemcpy(d_channel_coeffs, h_channel_coeffs, channel_size_bytes, cudaMemcpyHostToDevice));
+
+  dim3 threads_multipath(512, 1);
+  dim3 blocks_multipath((num_samples + threads_multipath.x - 1) / threads_multipath.x, nb_rx);
+  size_t sharedMemSize = (threads_multipath.x + channel_length - 1) * sizeof(float2);
+  multipath_channel_kernel<<<blocks_multipath, threads_multipath, sharedMemSize>>>(d_channel_coeffs,
+                                                                                   kernel_input_ptr,
+                                                                                   d_intermediate_sig,
+                                                                                   num_samples,
+                                                                                   channel_length,
+                                                                                   nb_tx,
+                                                                                   nb_rx);
+
+  dim3 threads_noise(256, 1);
+  dim3 blocks_noise((num_samples + threads_noise.x - 1) / threads_noise.x, nb_rx);
+  float pn_variance = 1e-5f * 2.0f * 3.1415926535f * 300.0f * (float)ts;
+  bool apply_phase_noise = (pdu_bit_map & ptrs_bit_map);
+  add_noise_and_phase_noise_kernel<<<blocks_noise, threads_noise>>>(d_intermediate_sig,
+                                                                    d_final_output,
+                                                                    d_curand_states,
+                                                                    num_samples,
+                                                                    sqrtf(sigma2 / 2.0f),
+                                                                    sqrtf(pn_variance),
+                                                                    apply_phase_noise);
+
+  cudaDeviceSynchronize();
+
+  // If output_signal is NULL, the caller intends to keep the data on the GPU
+  // for further processing (e.g., summing outputs). Otherwise, copy back to host.
+  if (output_signal != NULL) {
+#if defined(USE_UNIFIED_MEMORY)
+    short2 *h_final_output_pinned = (short2 *)h_final_output_pinned_void;
+    for (int ii = 0; ii < nb_rx; ii++) {
+      memcpy(output_signal[ii] + slot_offset + delay, h_final_output_pinned + ii * num_samples, num_samples * sizeof(short2));
+    }
+#else
+    short2 *h_final_output_pinned = (short2 *)h_final_output_pinned_void;
+    CHECK_CUDA(cudaMemcpy(h_final_output_pinned, d_final_output, nb_rx * num_samples * sizeof(short2), cudaMemcpyDeviceToHost));
+    for (int ii = 0; ii < nb_rx; ii++) {
+      memcpy(output_signal[ii] + slot_offset + delay, h_final_output_pinned + ii * num_samples, num_samples * sizeof(short2));
+    }
+#endif
+  }
+}
+
+void sum_channel_outputs_cuda(void **d_individual_outputs, void *d_final_output, int num_channels, int nb_rx, int num_samples)
+{
+  void **d_ptr_array;
+  size_t ptr_array_size = num_channels * sizeof(void *);
+  CHECK_CUDA(cudaMalloc(&d_ptr_array, ptr_array_size));
+
+  CHECK_CUDA(cudaMemcpy(d_ptr_array, d_individual_outputs, ptr_array_size, cudaMemcpyHostToDevice));
+
+  int num_total_samples = nb_rx * num_samples;
+  dim3 threads(256, 1);
+  dim3 blocks((num_total_samples + threads.x - 1) / threads.x, 1);
+
+  sum_outputs_kernel<<<blocks, threads>>>((const short2 **)d_ptr_array, (short2 *)d_final_output, num_channels, num_total_samples);
+
+  CHECK_CUDA(cudaFree(d_ptr_array));
+}
+
+void run_channel_pipeline_cuda_streamed(int nb_tx,
+                                        int nb_rx,
+                                        int channel_length,
+                                        uint32_t num_samples,
+                                        float *h_channel_coeffs,
+                                        float sigma2,
+                                        double ts,
+                                        uint16_t pdu_bit_map,
+                                        uint16_t ptrs_bit_map,
+                                        void *d_tx_sig_void,
+                                        void *d_intermediate_sig_void,
+                                        void *d_final_output_void,
+                                        void *d_curand_states_void,
+                                        void *h_tx_sig_pinned_void,
+                                        void *d_channel_coeffs_void,
+                                        void *stream_void)
+{
+  cudaStream_t stream = (cudaStream_t)stream_void;
+  float2 *d_intermediate_sig = (float2 *)d_intermediate_sig_void;
+  short2 *d_final_output = (short2 *)d_final_output_void;
+  curandState_t *d_curand_states = (curandState_t *)d_curand_states_void;
+  float2 *d_channel_coeffs = (float2 *)d_channel_coeffs_void;
+  float *kernel_input_ptr;
+
+#if defined(USE_UNIFIED_MEMORY) || defined(USE_ATS_MEMORY)
+  kernel_input_ptr = (float *)h_tx_sig_pinned_void;
+#else
+  float *d_tx_sig = (float *)d_tx_sig_void;
+  float *h_tx_sig_pinned = (float *)h_tx_sig_pinned_void;
+
+  const int padding_len = channel_length - 1;
+  const size_t total_padded_tx_bytes = nb_tx * (num_samples + padding_len) * 2 * sizeof(float);
+
+  CHECK_CUDA(cudaMemcpyAsync(d_tx_sig, h_tx_sig_pinned, total_padded_tx_bytes, cudaMemcpyHostToDevice, stream));
+  kernel_input_ptr = d_tx_sig;
+#endif
+
+  size_t channel_size_bytes = nb_tx * nb_rx * channel_length * sizeof(float2);
+  CHECK_CUDA(cudaMemcpyAsync(d_channel_coeffs, h_channel_coeffs, channel_size_bytes, cudaMemcpyHostToDevice, stream));
+
+  dim3 threads_multipath(512, 1);
+  dim3 blocks_multipath((num_samples + threads_multipath.x - 1) / threads_multipath.x, nb_rx);
+  size_t sharedMemSize = (threads_multipath.x + channel_length - 1) * sizeof(float2);
+  multipath_channel_kernel<<<blocks_multipath, threads_multipath, sharedMemSize, stream>>>(d_channel_coeffs,
+                                                                                           kernel_input_ptr,
+                                                                                           d_intermediate_sig,
+                                                                                           num_samples,
+                                                                                           channel_length,
+                                                                                           nb_tx,
+                                                                                           nb_rx);
+
+  dim3 threads_noise(256, 1);
+  dim3 blocks_noise((num_samples + threads_noise.x - 1) / threads_noise.x, nb_rx);
+  float pn_variance = 1e-5f * 2.0f * 3.1415926535f * 300.0f * (float)ts;
+  bool apply_phase_noise = (pdu_bit_map & ptrs_bit_map);
+  add_noise_and_phase_noise_kernel<<<blocks_noise, threads_noise, 0, stream>>>(d_intermediate_sig,
+                                                                               d_final_output,
+                                                                               d_curand_states,
+                                                                               num_samples,
+                                                                               sqrtf(sigma2 / 2.0f),
+                                                                               sqrtf(pn_variance),
+                                                                               apply_phase_noise);
+}
+
+void run_channel_pipeline_cuda_batched(int num_channels,
+                                       int nb_tx,
+                                       int nb_rx,
+                                       int channel_length,
+                                       uint32_t num_samples,
+                                       void *d_channel_coeffs_batch,
+                                       float sigma2,
+                                       double ts,
+                                       uint16_t pdu_bit_map,
+                                       uint16_t ptrs_bit_map,
+                                       void *d_tx_sig_batch,
+                                       void *d_intermediate_sig_batch,
+                                       void *d_final_output_batch,
+                                       void *d_curand_states)
+{
+  float2 *d_tx = (float2 *)d_tx_sig_batch;
+  float2 *d_intermediate = (float2 *)d_intermediate_sig_batch;
+  short2 *d_final = (short2 *)d_final_output_batch;
+  float2 *d_coeffs = (float2 *)d_channel_coeffs_batch;
+  curandState_t *d_states = (curandState_t *)d_curand_states;
+
+  dim3 threads_multipath(512, 1, 1);
+  dim3 blocks_multipath((num_samples + threads_multipath.x - 1) / threads_multipath.x, nb_rx, num_channels);
+  size_t sharedMemSize = (threads_multipath.x + channel_length - 1) * sizeof(float2);
+
+  multipath_channel_kernel_batched<<<blocks_multipath, threads_multipath, sharedMemSize>>>(d_coeffs,
+                                                                                           d_tx,
+                                                                                           d_intermediate,
+                                                                                           num_samples,
+                                                                                           channel_length,
+                                                                                           nb_tx,
+                                                                                           nb_rx);
+
+  dim3 threads_noise(256, 1, 1);
+  dim3 blocks_noise((num_samples + threads_noise.x - 1) / threads_noise.x, nb_rx, num_channels);
+  float pn_variance = 1e-5f * 2.0f * 3.1415926535f * 300.0f * (float)ts;
+  bool apply_phase_noise = (pdu_bit_map & ptrs_bit_map);
+  add_noise_and_phase_noise_kernel_batched<<<blocks_noise, threads_noise>>>(d_intermediate,
+                                                                            d_final,
+                                                                            d_states,
+                                                                            num_samples,
+                                                                            nb_rx,
+                                                                            sqrtf(sigma2 / 2.0f),
+                                                                            sqrtf(pn_variance),
+                                                                            apply_phase_noise);
+
+  // Synchronization happens in the benchmark (test_channel_scalability) after this call returns.
+}
+} // extern "C"

openair1/SIMULATION/TOOLS/multipath_channel.c

@@ -28,147 +28,144 @@
 
 uint8_t multipath_channel_nosigconv(channel_desc_t *desc)
 {
-  random_channel(desc,0);
-  return(1);
+  random_channel(desc, 0);
+  return (1);
+}
+
+void interleave_channel_output(float **rx_sig_re, float **rx_sig_im, float **output_interleaved, int nb_rx, int num_samples)
+{
+  for (int i = 0; i < nb_rx; i++) {
+    for (int j = 0; j < num_samples; j++) {
+      output_interleaved[i][2 * j] = rx_sig_re[i][j];
+      output_interleaved[i][2 * j + 1] = rx_sig_im[i][j];
+    }
+  }
 }
 
 //#define CHANNEL_SSE
+
 #ifdef CHANNEL_SSE
-void __attribute__ ((no_sanitize_address)) multipath_channel(channel_desc_t *desc,
-                       double tx_sig_re[NB_ANTENNAS_TX][30720*2],
-                       double tx_sig_im[NB_ANTENANS_TX][30720*2],
-                       double rx_sig_re[NB_ANTENNAS_RX][30720*2],
-                       double rx_sig_im[NB_ANTENNAS_RX][30720*2],
-                       uint32_t length,
-                       uint8_t keep_channel,
-             		       int log_channel)
+void __attribute__((no_sanitize_address)) multipath_channel(channel_desc_t *desc,
+                                                            double **tx_sig_re,
+                                                            double **tx_sig_im,
+                                                            double **rx_sig_re,
+                                                            double **rx_sig_im,
+                                                            uint32_t length,
+                                                            uint8_t keep_channel,
+                                                            int log_channel)
 {
+  int i, ii, j, l;
+  // int simd_length;
+  // int simd_length, tail_start;
+  simde__m128d rx_tmp128_re, rx_tmp128_im, tx128_re, tx128_im, ch128_r, ch128_i, pathloss128;
 
-  int i,ii,j,l;
-  int length1, length2, tail;
-  simde__m128d rx_tmp128_re_f, rx_tmp128_im_f, rx_tmp128_re, rx_tmp128_im, rx_tmp128_1, rx_tmp128_2, rx_tmp128_3, rx_tmp128_4,
-      tx128_re, tx128_im, ch128_x, ch128_y, pathloss128;
-
-  double path_loss = pow(10,desc->path_loss_dB/20);
+  double path_loss = pow(10, desc->path_loss_dB / 20);
   uint64_t dd = desc->channel_offset;
 
   pathloss128 = simde_mm_set1_pd(path_loss);
 
-#ifdef DEBUG_CH
-  printf("[CHANNEL] keep = %d : path_loss = %g (%f), nb_rx %d, nb_tx %d, dd %lu, len %d \n",keep_channel,path_loss,desc->path_loss_dB,desc->nb_rx,desc->nb_tx,dd,desc->channel_length);
-#endif
-
-  if (keep_channel) {
-    // do nothing - keep channel
-  } else {
-    random_channel(desc,0);
+  if (keep_channel == 0) {
+    random_channel(desc, 0);
   }
 
-  start_meas(&desc->convolution);
+  // simd_length = (length - dd) / 2;
+  // tail_start = simd_length * 2;
 
-#ifdef DEBUG_CH
+  for (i = 0; i < (int)(length - dd); i += 2) {
+    for (ii = 0; ii < desc->nb_rx; ii++) {
+      rx_tmp128_re = simde_mm_setzero_pd();
+      rx_tmp128_im = simde_mm_setzero_pd();
 
-  for (int l = 0; l<(int)desc->channel_length; l++) {
-    printf("%p (%f,%f) ",desc->ch[0],desc->ch[0][l].x,desc->ch[0][l].y);
-  }
-
-  printf("\n");
-#endif
-
-  tail = ((int)length-dd)%2;
-
-  if(tail)
-    length1 = ((int)length-dd)-1;
-  else
-    length1 = ((int)length-dd);
-
-  length2 = length1/2;
-
-  for (i=0; i<length2; i++) { //
-    for (ii=0; ii<desc->nb_rx; ii++) {
-      // rx_tmp.x = 0;
-      // rx_tmp.y = 0;
-      rx_tmp128_re_f = simde_mm_setzero_pd();
-      rx_tmp128_im_f = simde_mm_setzero_pd();
-
-      for (j=0; j<desc->nb_tx; j++) {
-        for (l = 0; l<(int)desc->channel_length; l++) {
-          if ((i>=0) && (i-l)>=0) { //SIMD correct only if length1 > 2*channel_length...which is almost always satisfied
-            // tx.x = tx_sig_re[j][i-l];
-            // tx.y = tx_sig_im[j][i-l];
-            tx128_re = simde_mm_loadu_pd(&tx_sig_re[j][2 * i - l]); // tx_sig_re[j][i-l+1], tx_sig_re[j][i-l]
-            tx128_im = simde_mm_loadu_pd(&tx_sig_im[j][2 * i - l]);
-          } else {
-            //tx.x =0;
-            //tx.y =0;
-            tx128_re = simde_mm_setzero_pd();
-            tx128_im = simde_mm_setzero_pd();
+      for (i = 0; i < (int)(length - dd); i += 2) {
+        for (ii = 0; ii < desc->nb_rx; ii++) {
+          rx_tmp128_re = simde_mm_setzero_pd();
+          rx_tmp128_im = simde_mm_setzero_pd();
+          for (j = 0; j < desc->nb_tx; j++) {
+            for (l = 0; l < (int)desc->channel_length; l++) {
+              double tx_re[2] = {0.0, 0.0}, tx_im[2] = {0.0, 0.0};
+              if ((i - l) >= 0)
+                tx_re[0] = tx_sig_re[j][i - l];
+              if ((i + 1 - l) >= 0)
+                tx_re[1] = tx_sig_re[j][i + 1 - l];
+              if ((i - l) >= 0)
+                tx_im[0] = tx_sig_im[j][i - l];
+              if ((i + 1 - l) >= 0)
+                tx_im[1] = tx_sig_im[j][i + 1 - l];
+              tx128_re = simde_mm_loadu_pd(tx_re);
+              tx128_im = simde_mm_loadu_pd(tx_im);
+              ch128_r = simde_mm_set1_pd(desc->ch[ii + (j * desc->nb_rx)][l].r);
+              ch128_i = simde_mm_set1_pd(desc->ch[ii + (j * desc->nb_rx)][l].i);
+              rx_tmp128_re =
+                  simde_mm_add_pd(rx_tmp128_re,
+                                  simde_mm_sub_pd(simde_mm_mul_pd(tx128_re, ch128_r), simde_mm_mul_pd(tx128_im, ch128_i)));
+              rx_tmp128_im =
+                  simde_mm_add_pd(rx_tmp128_im,
+                                  simde_mm_add_pd(simde_mm_mul_pd(tx128_re, ch128_i), simde_mm_mul_pd(tx128_im, ch128_r)));
+            }
           }
-
-          ch128_x = simde_mm_set1_pd(desc->ch[ii + (j * desc->nb_rx)][l].x);
-          ch128_y = simde_mm_set1_pd(desc->ch[ii + (j * desc->nb_rx)][l].y);
-          //  rx_tmp.x += (tx.x * desc->ch[ii+(j*desc->nb_rx)][l].x) - (tx.y * desc->ch[ii+(j*desc->nb_rx)][l].y);
-          //  rx_tmp.y += (tx.y * desc->ch[ii+(j*desc->nb_rx)][l].x) + (tx.x * desc->ch[ii+(j*desc->nb_rx)][l].y);
-          rx_tmp128_1 = simde_mm_mul_pd(tx128_re, ch128_x);
-          rx_tmp128_2 = simde_mm_mul_pd(tx128_re, ch128_y);
-          rx_tmp128_3 = simde_mm_mul_pd(tx128_im, ch128_x);
-          rx_tmp128_4 = simde_mm_mul_pd(tx128_im, ch128_y);
-          rx_tmp128_re = simde_mm_sub_pd(rx_tmp128_1, rx_tmp128_4);
-          rx_tmp128_im = simde_mm_add_pd(rx_tmp128_2, rx_tmp128_3);
-          rx_tmp128_re_f = simde_mm_add_pd(rx_tmp128_re_f, rx_tmp128_re);
-          rx_tmp128_im_f = simde_mm_add_pd(rx_tmp128_im_f, rx_tmp128_im);
-        } //l
-      }  // j
-
-      //rx_sig_re[ii][i+dd] = rx_tmp.x*path_loss;
-      //rx_sig_im[ii][i+dd] = rx_tmp.y*path_loss;
-      rx_tmp128_re_f = simde_mm_mul_pd(rx_tmp128_re_f, pathloss128);
-      rx_tmp128_im_f = simde_mm_mul_pd(rx_tmp128_im_f, pathloss128);
-      simde_mm_storeu_pd(&rx_sig_re[ii][2 * i + dd], rx_tmp128_re_f); // max index: length-dd -1 + dd = length -1
-      simde_mm_storeu_pd(&rx_sig_im[ii][2 * i + dd], rx_tmp128_im_f);
-      /*
-      if ((ii==0)&&((i%32)==0)) {
-      printf("%p %p %f,%f => %e,%e\n",rx_sig_re[ii],rx_sig_im[ii],rx_tmp.x,rx_tmp.y,rx_sig_re[ii][i-dd],rx_sig_im[ii][i-dd]);
+          simde_mm_storeu_pd(&rx_sig_re[ii][i + dd], simde_mm_mul_pd(rx_tmp128_re, pathloss128));
+          simde_mm_storeu_pd(&rx_sig_im[ii][i + dd], simde_mm_mul_pd(rx_tmp128_im, pathloss128));
+        }
       }
-      */
-      //rx_sig_re[ii][i] = sqrt(.5)*(tx_sig_re[0][i] + tx_sig_re[1][i]);
-      //rx_sig_im[ii][i] = sqrt(.5)*(tx_sig_im[0][i] + tx_sig_im[1][i]);
-
     } // ii
   } // i
 
-  stop_meas(&desc->convolution);
-
+  // Handle the final sample if the length is odd
+  if ((length - dd) % 2) {
+    int i_tail = length - dd - 1;
+    for (ii = 0; ii < desc->nb_rx; ii++) {
+      struct complexd rx_tmp = {0};
+      for (j = 0; j < desc->nb_tx; j++) {
+        struct complexd *chan = desc->ch[ii + (j * desc->nb_rx)];
+        for (l = 0; l < (int)desc->channel_length; l++) {
+          if ((i_tail - l) >= 0) {
+            struct complexd tx;
+            tx.r = tx_sig_re[j][i_tail - l];
+            tx.i = tx_sig_im[j][i_tail - l];
+            rx_tmp.r += (tx.r * chan[l].r) - (tx.i * chan[l].i);
+            rx_tmp.i += (tx.i * chan[l].r) + (tx.r * chan[l].i);
+          }
+        }
+      }
+      rx_sig_re[ii][i_tail + dd] = rx_tmp.r * path_loss;
+      rx_sig_im[ii][i_tail + dd] = rx_tmp.i * path_loss;
+    }
+  }
 }
 
 #else
 
-void __attribute__ ((no_sanitize_address)) multipath_channel(channel_desc_t *desc,
-                       double *tx_sig_re[NB_ANTENNAS_TX],
-                       double *tx_sig_im[NB_ANTENNAS_TX],
-                       double *rx_sig_re[NB_ANTENNAS_RX],
-                       double *rx_sig_im[NB_ANTENNAS_RX],
-                       uint32_t length,
-                       uint8_t keep_channel,
-		                   int log_channel)
+void __attribute__((no_sanitize_address)) multipath_channel(channel_desc_t *desc,
+                                                            double **tx_sig_re,
+                                                            double **tx_sig_im,
+                                                            double **rx_sig_re,
+                                                            double **rx_sig_im,
+                                                            uint32_t length,
+                                                            uint8_t keep_channel,
+                                                            int log_channel)
 {
-
-  double path_loss = pow(10,desc->path_loss_dB/20);
+  double path_loss = pow(10, desc->path_loss_dB / 20);
   uint64_t dd = desc->channel_offset;
 
 #ifdef DEBUG_CH
   printf("[CHANNEL] keep = %d : path_loss = %g (%f), nb_rx %d, nb_tx %d, dd %lu, len %d \n",
-         keep_channel, path_loss, desc->path_loss_dB, desc->nb_rx, desc->nb_tx, dd, desc->channel_length);
+         keep_channel,
+         path_loss,
+         desc->path_loss_dB,
+         desc->nb_rx,
+         desc->nb_tx,
+         dd,
+         desc->channel_length);
 #endif
 
   if (keep_channel) {
     // do nothing - keep channel
   } else {
-    random_channel(desc,0);
+    random_channel(desc, 0);
   }
 
 #ifdef DEBUG_CH
-  for (int l = 0; l<(int)desc->channel_length; l++) {
+  for (int l = 0; l < (int)desc->channel_length; l++) {
     printf("ch[%i] = (%f, %f)\n", l, desc->ch[0][l].r, desc->ch[0][l].i);
   }
 #endif
@@ -178,27 +175,27 @@ void __attribute__ ((no_sanitize_address)) multipath_channel(channel_desc_t *des
     get_cexp_doppler(cexp_doppler, desc, length);
   }
 
-  for (int i=0; i<((int)length-dd); i++) {
-    for (int ii=0; ii<desc->nb_rx; ii++) {
-      struct complexd rx_tmp={0};
-      for (int j=0; j<desc->nb_tx; j++) {
-        struct complexd *chan=desc->ch[ii+(j*desc->nb_rx)];
-        for (int l = 0; l<(int)desc->channel_length; l++) {
-          if ((i>=0) && (i-l)>=0) {
+  for (int i = 0; i < ((int)length - dd); i++) {
+    for (int ii = 0; ii < desc->nb_rx; ii++) {
+      struct complexd rx_tmp = {0};
+      for (int j = 0; j < desc->nb_tx; j++) {
+        struct complexd *chan = desc->ch[ii + (j * desc->nb_rx)];
+        for (int l = 0; l < (int)desc->channel_length; l++) {
+          if ((i >= 0) && (i - l) >= 0) {
             struct complexd tx;
-            tx.r = tx_sig_re[j][i-l];
-            tx.i = tx_sig_im[j][i-l];
+            tx.r = tx_sig_re[j][i - l];
+            tx.i = tx_sig_im[j][i - l];
             rx_tmp.r += (tx.r * chan[l].r) - (tx.i * chan[l].i);
             rx_tmp.i += (tx.i * chan[l].r) + (tx.r * chan[l].i);
           }
-          #if 0
+#if 0
           if (i==0 && log_channel == 1) {
             printf("channel[%d][%d][%d] = %f dB \t(%e, %e)\n",
                    ii, j, l, 10 * log10(pow(chan[l].r, 2.0) + pow(chan[l].i, 2.0)), chan[l].r, chan[l].i);
 	        }
-          #endif
-        } //l
-      }  // j
+#endif
+        } // l
+      } // j
 #if 0
       if (desc->max_Doppler != 0.0)
         rx_tmp = cdMul(rx_tmp, cexp_doppler[i]);
@@ -206,26 +203,190 @@ void __attribute__ ((no_sanitize_address)) multipath_channel(channel_desc_t *des
 
 #ifdef DOPPLER_DEBUG
       printf("[k %2i] cexp_doppler = (%7.4f, %7.4f), abs(cexp_doppler) = %.4f\n",
-                   i,
-                   cexp_doppler[i].r,
-                   cexp_doppler[i].i,
-                   sqrt(cexp_doppler[i].r * cexp_doppler[i].r + cexp_doppler[i].i * cexp_doppler[i].i));
+             i,
+             cexp_doppler[i].r,
+             cexp_doppler[i].i,
+             sqrt(cexp_doppler[i].r * cexp_doppler[i].r + cexp_doppler[i].i * cexp_doppler[i].i));
 #endif
 
-      rx_sig_re[ii][i+dd] = rx_tmp.r*path_loss;
-      rx_sig_im[ii][i+dd] = rx_tmp.i*path_loss;
+      rx_sig_re[ii][i + dd] = rx_tmp.r * path_loss;
+      rx_sig_im[ii][i + dd] = rx_tmp.i * path_loss;
 #ifdef DEBUG_CH
-      if ((i%32)==0) {
-	       printf("rx aa %d: %f, %f  =>  %e, %e\n",
-                ii,  rx_tmp.r, rx_tmp.i, rx_sig_re[ii][i-dd], rx_sig_im[ii][i-dd]);
-      }	
-#endif      
-      //rx_sig_re[ii][i] = sqrt(.5)*(tx_sig_re[0][i] + tx_sig_re[1][i]);
-      //rx_sig_im[ii][i] = sqrt(.5)*(tx_sig_im[0][i] + tx_sig_im[1][i]);
+      if ((i % 32) == 0) {
+        printf("rx aa %d: %f, %f  =>  %e, %e\n", ii, rx_tmp.r, rx_tmp.i, rx_sig_re[ii][i - dd], rx_sig_im[ii][i - dd]);
+      }
+#endif
+      // rx_sig_re[ii][i] = sqrt(.5)*(tx_sig_re[0][i] + tx_sig_re[1][i]);
+      // rx_sig_im[ii][i] = sqrt(.5)*(tx_sig_im[0][i] + tx_sig_im[1][i]);
 
     } // ii
   } // i
 }
 #endif
 
+#ifdef CHANNEL_SSE
+void __attribute__((no_sanitize_address)) multipath_channel_float(channel_desc_t *desc,
+                                                                  float **tx_sig_interleaved,
+                                                                  float **rx_sig_re,
+                                                                  float **rx_sig_im,
+                                                                  uint32_t length,
+                                                                  uint8_t keep_channel,
+                                                                  int log_channel)
+{
+  int i, ii, j, l;
+  simde__m128 rx_tmp128_re, rx_tmp128_im, tx128_re, tx128_im, ch128_r, ch128_i, pathloss128;
+  uint64_t dd = desc->channel_offset;
 
+  if (keep_channel == 0) {
+    random_channel(desc, 0);
+  }
+
+  struct complexd cexp_doppler[length];
+  if (desc->max_Doppler != 0.0) {
+    get_cexp_doppler(cexp_doppler, desc, length);
+  }
+
+  if (dd >= length) {
+    return; // No samples to process
+  }
+
+  for (i = 0; i <= (int)(length - dd) - 4; i += 4) {
+    for (ii = 0; ii < desc->nb_rx; ii++) {
+      rx_tmp128_re = simde_mm_setzero_ps();
+      rx_tmp128_im = simde_mm_setzero_ps();
+
+      for (j = 0; j < desc->nb_tx; j++) {
+        for (l = 0; l < (int)desc->channel_length; l++) {
+          int idx = i - l;
+          if (idx >= 0) {
+            // Load 2 complex numbers (4 floats) => {I0, Q0, I1, Q1}
+            simde__m128 vec0 = simde_mm_loadu_ps(&tx_sig_interleaved[j][2 * idx]);
+            simde__m128 vec1 = simde_mm_loadu_ps(&tx_sig_interleaved[j][2 * (idx + 2)]);
+            tx128_re = simde_mm_shuffle_ps(vec0, vec1, SIMDE_MM_SHUFFLE(2, 0, 2, 0)); // {I0,I1,I2,I3}
+            tx128_im = simde_mm_shuffle_ps(vec0, vec1, SIMDE_MM_SHUFFLE(3, 1, 3, 1)); // {Q0,Q1,Q2,Q3}
+          } else {
+            tx128_re = simde_mm_setzero_ps();
+            tx128_im = simde_mm_setzero_ps();
+          }
+
+          ch128_r = simde_mm_set1_ps((float)desc->ch[ii + (j * desc->nb_rx)][l].r);
+          ch128_i = simde_mm_set1_ps((float)desc->ch[ii + (j * desc->nb_rx)][l].i);
+
+          // re = (tx_re * ch_r) - (tx_im * ch_i)
+          rx_tmp128_re = simde_mm_add_ps(rx_tmp128_re,
+                                         simde_mm_sub_ps(simde_mm_mul_ps(tx128_re, ch128_r), simde_mm_mul_ps(tx128_im, ch128_i)));
+          // im = (tx_re * ch_i) + (tx_im * ch_r)
+          rx_tmp128_im = simde_mm_add_ps(rx_tmp128_im,
+                                         simde_mm_add_ps(simde_mm_mul_ps(tx128_re, ch128_i), simde_mm_mul_ps(tx128_im, ch128_r)));
+        }
+      }
+
+#if 0
+            if (desc->max_Doppler != 0.0) {
+                float doppler_re[4] = {(float)cexp_doppler[i].r, (float)cexp_doppler[i+1].r, (float)cexp_doppler[i+2].r, (float)cexp_doppler[i+3].r};
+                float doppler_im[4] = {(float)cexp_doppler[i].i, (float)cexp_doppler[i+1].i, (float)cexp_doppler[i+2].i, (float)cexp_doppler[i+3].i};
+                
+                simde__m128 doppler128_r = simde_mm_loadu_ps(doppler_re);
+                simde__m128 doppler128_i = simde_mm_loadu_ps(doppler_im);
+
+                simde__m128 temp_re = rx_tmp128_re;
+                simde__m128 temp_im = rx_tmp128_im;
+
+                rx_tmp128_re = simde_mm_sub_ps(simde_mm_mul_ps(temp_re, doppler128_r), simde_mm_mul_ps(temp_im, doppler128_i));
+                rx_tmp128_im = simde_mm_add_ps(simde_mm_mul_ps(temp_im, doppler128_r), simde_mm_mul_ps(temp_re, doppler128_i));
+            }
+#endif
+      simde_mm_storeu_ps(&rx_sig_re[ii][i + dd], rx_tmp128_re);
+      simde_mm_storeu_ps(&rx_sig_im[ii][i + dd], rx_tmp128_im);
+    }
+  }
+
+  // Handle remaining samples (tail loop) that are not a multiple of 4
+  for (; i < (int)(length - dd); i++) {
+    for (ii = 0; ii < desc->nb_rx; ii++) {
+      struct complexf rx_tmp = {0.0f, 0.0f};
+      for (j = 0; j < desc->nb_tx; j++) {
+        struct complexd *chan = desc->ch[ii + (j * desc->nb_rx)];
+        for (l = 0; l < (int)desc->channel_length; l++) {
+          if ((i - l) >= 0) {
+            struct complexf tx;
+            tx.r = tx_sig_interleaved[j][2 * (i - l)];
+            tx.i = tx_sig_interleaved[j][2 * (i - l) + 1];
+            rx_tmp.r += (tx.r * (float)chan[l].r) - (tx.i * (float)chan[l].i);
+            rx_tmp.i += (tx.i * (float)chan[l].r) + (tx.r * (float)chan[l].i);
+          }
+        }
+      }
+#if 0
+            if (desc->max_Doppler != 0.0) {
+                struct complexf doppler_factor = {(float)cexp_doppler[i].r, (float)cexp_doppler[i].i};
+                struct complexf temp = rx_tmp;
+                rx_tmp.r = (temp.r * doppler_factor.r) - (temp.i * doppler_factor.i);
+                rx_tmp.i = (temp.i * doppler_factor.r) + (temp.r * doppler_factor.i);
+            }
+#endif
+      rx_sig_re[ii][i + dd] = rx_tmp.r;
+      rx_sig_im[ii][i + dd] = rx_tmp.i;
+    }
+  }
+}
+
+#else
+void multipath_channel_float(channel_desc_t *desc,
+                             float **tx_sig_interleaved,
+                             float **rx_sig_re,
+                             float **rx_sig_im,
+                             uint32_t length,
+                             uint8_t keep_channel,
+                             int log_channel)
+{
+  uint64_t dd = desc->channel_offset;
+
+  if (keep_channel == 0) {
+    random_channel(desc, 0);
+  }
+
+  struct complexd cexp_doppler[length];
+  if (desc->max_Doppler != 0.0) {
+    get_cexp_doppler(cexp_doppler, desc, length);
+  }
+
+  if (dd >= length) {
+    return;
+  }
+
+  for (int i = 0; i < ((int)length - dd); i++) {
+    for (int ii = 0; ii < desc->nb_rx; ii++) {
+      struct complexf rx_tmp = {0.0f, 0.0f};
+
+      for (int j = 0; j < desc->nb_tx; j++) {
+        struct complexd *chan = desc->ch[ii + (j * desc->nb_rx)];
+
+        for (int l = 0; l < (int)desc->channel_length; l++) {
+          if ((i - l) >= 0) {
+            struct complexf tx;
+            tx.r = tx_sig_interleaved[j][2 * (i - l)];
+            tx.i = tx_sig_interleaved[j][2 * (i - l) + 1];
+            rx_tmp.r += (tx.r * (float)chan[l].r) - (tx.i * (float)chan[l].i);
+            rx_tmp.i += (tx.i * (float)chan[l].r) + (tx.r * (float)chan[l].i);
+          }
+        } // l (channel_length)
+      } // j (nb_tx)
+
+#if 0
+            if (desc->max_Doppler != 0.0) {
+                // Perform complex multiplication: rx_tmp = rx_tmp * cexp_doppler[i]
+                struct complexf doppler_factor = {(float)cexp_doppler[i].r, (float)cexp_doppler[i].i};
+                struct complexf temp = rx_tmp;
+                rx_tmp.r = (temp.r * doppler_factor.r) - (temp.i * doppler_factor.i);
+                rx_tmp.i = (temp.i * doppler_factor.r) + (temp.r * doppler_factor.i);
+            }
+#endif
+      rx_sig_re[ii][i + dd] = rx_tmp.r;
+      rx_sig_im[ii][i + dd] = rx_tmp.i;
+
+    } // ii (nb_rx)
+  } // i (length)
+}
+
+#endif

openair1/SIMULATION/TOOLS/multipath_channel.cu

@@ -0,0 +1,272 @@
+/*
+ * Licensed to the OpenAirInterface (OAI) Software Alliance under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The OpenAirInterface Software Alliance licenses this file to You under
+ * the OAI Public License, Version 1.1  (the "License"); you may not use this file
+ * except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.openairinterface.org/?page_id=698
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *-------------------------------------------------------------------------------
+ * For more information about the OpenAirInterface (OAI) Software Alliance:
+ *      contact@openairinterface.org
+ */
+
+#include <stdio.h>
+#include <cuda_runtime.h>
+#include "oai_cuda.h"
+
+#define CHECK_CUDA(val) checkCuda((val), #val, __FILE__, __LINE__)
+static void checkCuda(cudaError_t result, const char *const func, const char *const file, const int line)
+{
+  if (result != cudaSuccess) {
+    fprintf(stderr,
+            "CUDA Error at %s:%d code=%d(%s) \"%s\" \n",
+            file,
+            line,
+            static_cast<unsigned int>(result),
+            cudaGetErrorString(result),
+            func);
+    cudaDeviceReset();
+    exit(EXIT_FAILURE);
+  }
+}
+
+__device__ __forceinline__ float2 complex_mul(float2 a, float2 b)
+{
+  return make_float2(a.x * b.x - a.y * b.y, a.x * b.y + a.y * b.x);
+}
+
+__device__ __forceinline__ float2 complex_add(float2 a, float2 b)
+{
+  return make_float2(a.x + b.x, a.y + b.y);
+}
+
+__global__ void interleave_output_kernel(const float *__restrict__ rx_re,
+                                         const float *__restrict__ rx_im,
+                                         float2 *__restrict__ output_interleaved,
+                                         int num_total_samples)
+{
+  const int i = blockIdx.x * blockDim.x + threadIdx.x;
+  if (i < num_total_samples) {
+    output_interleaved[i].x = rx_re[i];
+    output_interleaved[i].y = rx_im[i];
+  }
+}
+
+__global__ void multipath_channel_kernel(const float2 *__restrict__ d_channel_coeffs,
+                                         const float *__restrict__ tx_sig,
+                                         float2 *__restrict__ rx_sig,
+                                         int num_samples,
+                                         int channel_length,
+                                         int nb_tx,
+                                         int nb_rx)
+{
+  extern __shared__ float2 tx_shared[];
+  const int i = blockIdx.x * blockDim.x + threadIdx.x;
+  const int rx_ant_idx = blockIdx.y;
+  const int padding_len = channel_length - 1;
+  const int padded_num_samples = num_samples + padding_len;
+
+  if (i >= num_samples)
+    return;
+
+  float2 rx_tmp = make_float2(0.0f, 0.0f);
+
+  for (int j = 0; j < nb_tx; j++) {
+    const int tid = threadIdx.x;
+    const int block_start_idx = blockIdx.x * blockDim.x;
+    const int shared_mem_size = blockDim.x + channel_length - 1;
+
+    for (int k = tid; k < shared_mem_size; k += blockDim.x) {
+      int load_idx = block_start_idx + k;
+      int interleaved_idx = 2 * (j * padded_num_samples + load_idx);
+      tx_shared[k] = make_float2(tx_sig[interleaved_idx], tx_sig[interleaved_idx + 1]);
+    }
+    __syncthreads();
+
+    for (int l = 0; l < channel_length; l++) {
+      float2 tx_sample = tx_shared[tid + (channel_length - 1) - l];
+      int chan_link_idx = rx_ant_idx + (j * nb_rx);
+      float2 chan_weight = d_channel_coeffs[chan_link_idx * channel_length + l];
+      rx_tmp = complex_add(rx_tmp, complex_mul(tx_sample, chan_weight));
+    }
+    __syncthreads();
+  }
+
+  rx_sig[rx_ant_idx * num_samples + i].x = rx_tmp.x;
+  rx_sig[rx_ant_idx * num_samples + i].y = rx_tmp.y;
+}
+
+__global__ void multipath_channel_kernel_batched(const float2 *__restrict__ d_channel_coeffs,
+                                                 const float2 *__restrict__ tx_sig,
+                                                 float2 *__restrict__ rx_sig,
+                                                 int num_samples,
+                                                 int channel_length,
+                                                 int nb_tx,
+                                                 int nb_rx)
+{
+  extern __shared__ float2 tx_shared[];
+
+  const int i = blockIdx.x * blockDim.x + threadIdx.x;
+  const int rx_ant_idx = blockIdx.y;
+  const int c = blockIdx.z;
+
+  if (i >= num_samples)
+    return;
+
+  float2 rx_tmp = make_float2(0.0f, 0.0f);
+
+  const int padding_len = channel_length - 1;
+  const int padded_num_samples = num_samples + padding_len;
+
+  const int channel_tx_offset = c * nb_tx * padded_num_samples;
+  const int channel_rx_offset = c * nb_rx * num_samples;
+
+  for (int j = 0; j < nb_tx; j++) {
+    const int tid = threadIdx.x;
+    const int block_start_idx = blockIdx.x * blockDim.x;
+    const int shared_mem_size = blockDim.x + channel_length - 1;
+
+    for (int k = tid; k < shared_mem_size; k += blockDim.x) {
+      int load_idx = block_start_idx + k;
+      tx_shared[k] = tx_sig[channel_tx_offset + j * padded_num_samples + load_idx];
+    }
+    __syncthreads();
+
+    for (int l = 0; l < channel_length; l++) {
+      float2 tx_sample = tx_shared[tid + (channel_length - 1) - l];
+      int chan_link_idx = (c * nb_tx * nb_rx) + (rx_ant_idx + j * nb_rx);
+      float2 chan_weight = d_channel_coeffs[chan_link_idx * channel_length + l];
+      rx_tmp = complex_add(rx_tmp, complex_mul(tx_sample, chan_weight));
+    }
+    __syncthreads();
+  }
+
+  rx_sig[channel_rx_offset + rx_ant_idx * num_samples + i].x = rx_tmp.x;
+  rx_sig[channel_rx_offset + rx_ant_idx * num_samples + i].y = rx_tmp.y;
+}
+
+extern "C" {
+
+void multipath_channel_cuda(float **rx_sig_re,
+                            float **rx_sig_im,
+                            int nb_tx,
+                            int nb_rx,
+                            int channel_length,
+                            uint32_t length,
+                            uint64_t channel_offset,
+                            float *h_channel_coeffs,
+                            void *d_tx_sig_void,
+                            void *d_rx_sig_void,
+                            void *d_channel_coeffs_void,
+                            void *h_tx_sig_pinned_void)
+{
+  float *d_tx_sig = (float *)d_tx_sig_void;
+  float2 *d_rx_sig = (float2 *)d_rx_sig_void;
+  float2 *d_channel_coeffs = (float2 *)d_channel_coeffs_void;
+  int num_samples = length - (int)channel_offset;
+  float *kernel_input_ptr;
+
+  const int padding_len = channel_length - 1;
+  const size_t total_padded_tx_bytes = nb_tx * (num_samples + padding_len) * 2 * sizeof(float);
+
+#if defined(USE_UNIFIED_MEMORY) || defined(USE_ATS_MEMORY)
+  kernel_input_ptr = (float *)h_tx_sig_pinned_void;
+#else // EXPLICIT COPY
+  float *h_tx_sig_pinned = (float *)h_tx_sig_pinned_void;
+  CHECK_CUDA(cudaMemcpy(d_tx_sig, h_tx_sig_pinned, total_padded_tx_bytes, cudaMemcpyHostToDevice));
+  kernel_input_ptr = d_tx_sig;
+#endif
+
+  size_t channel_size_bytes = nb_tx * nb_rx * channel_length * sizeof(float2);
+  CHECK_CUDA(cudaMemcpy(d_channel_coeffs, h_channel_coeffs, channel_size_bytes, cudaMemcpyHostToDevice));
+
+  dim3 threadsPerBlock(512, 1);
+  dim3 numBlocks((num_samples + threadsPerBlock.x - 1) / threadsPerBlock.x, nb_rx);
+  size_t sharedMemSize = (threadsPerBlock.x + channel_length - 1) * sizeof(float2);
+  multipath_channel_kernel<<<numBlocks, threadsPerBlock, sharedMemSize>>>(d_channel_coeffs,
+                                                                          kernel_input_ptr,
+                                                                          d_rx_sig,
+                                                                          num_samples,
+                                                                          channel_length,
+                                                                          nb_tx,
+                                                                          nb_rx);
+
+#if defined(USE_UNIFIED_MEMORY)
+  CHECK_CUDA(cudaDeviceSynchronize());
+  for (int ii = 0; ii < nb_rx; ii++) {
+    for (int i = 0; i < num_samples; i++) {
+      float2 result = d_rx_sig[ii * num_samples + i];
+      rx_sig_re[ii][i + channel_offset] = result.x;
+      rx_sig_im[ii][i + channel_offset] = result.y;
+    }
+  }
+#else
+  CHECK_CUDA(cudaDeviceSynchronize());
+  float2 *h_rx_sig = (float2 *)malloc(nb_rx * num_samples * sizeof(float2));
+  CHECK_CUDA(cudaMemcpy(h_rx_sig, d_rx_sig, nb_rx * num_samples * sizeof(float2), cudaMemcpyDeviceToHost));
+  for (int ii = 0; ii < nb_rx; ii++) {
+    for (int i = 0; i < num_samples; i++) {
+      float2 result = h_rx_sig[ii * num_samples + i];
+      rx_sig_re[ii][i + channel_offset] = result.x;
+      rx_sig_im[ii][i + channel_offset] = result.y;
+    }
+  }
+  free(h_rx_sig);
+#endif
+}
+
+void interleave_channel_output_cuda(float **rx_sig_re,
+                                    float **rx_sig_im,
+                                    void **output_interleaved_void,
+                                    int nb_rx,
+                                    int num_samples)
+{
+  float2 **output_interleaved = (float2 **)output_interleaved_void;
+
+  int num_total_samples = nb_rx * num_samples;
+  size_t total_bytes_per_plane = num_total_samples * sizeof(float);
+  size_t total_bytes_interleaved = num_total_samples * sizeof(float2);
+
+  float *h_re_flat = (float *)malloc(total_bytes_per_plane);
+  float *h_im_flat = (float *)malloc(total_bytes_per_plane);
+  float2 *h_out_flat = (float2 *)malloc(total_bytes_interleaved);
+
+  for (int i = 0; i < nb_rx; i++) {
+    memcpy(h_re_flat + i * num_samples, rx_sig_re[i], num_samples * sizeof(float));
+    memcpy(h_im_flat + i * num_samples, rx_sig_im[i], num_samples * sizeof(float));
+  }
+  float *d_re, *d_im;
+  float2 *d_out;
+  CHECK_CUDA(cudaMalloc((void **)&d_re, total_bytes_per_plane));
+  CHECK_CUDA(cudaMalloc((void **)&d_im, total_bytes_per_plane));
+  CHECK_CUDA(cudaMalloc((void **)&d_out, total_bytes_interleaved));
+  CHECK_CUDA(cudaMemcpy(d_re, h_re_flat, total_bytes_per_plane, cudaMemcpyHostToDevice));
+  CHECK_CUDA(cudaMemcpy(d_im, h_im_flat, total_bytes_per_plane, cudaMemcpyHostToDevice));
+
+  int threadsPerBlock = 512;
+  int blocksPerGrid = (num_total_samples + threadsPerBlock - 1) / threadsPerBlock;
+  interleave_output_kernel<<<blocksPerGrid, threadsPerBlock>>>(d_re, d_im, d_out, num_total_samples);
+
+  CHECK_CUDA(cudaMemcpy(h_out_flat, d_out, total_bytes_interleaved, cudaMemcpyDeviceToHost));
+
+  for (int i = 0; i < nb_rx; i++) {
+    memcpy(output_interleaved[i], h_out_flat + i * num_samples, num_samples * sizeof(float2));
+  }
+  free(h_re_flat);
+  free(h_im_flat);
+  free(h_out_flat);
+  CHECK_CUDA(cudaFree(d_re));
+  CHECK_CUDA(cudaFree(d_im));
+  CHECK_CUDA(cudaFree(d_out));
+}
+
+} // extern "C"

openair1/SIMULATION/TOOLS/oai_cuda.h

@@ -0,0 +1,193 @@
+/*
+ * Licensed to the OpenAirInterface (OAI) Software Alliance under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The OpenAirInterface Software Alliance licenses this file to You under
+ * the OAI Public License, Version 1.1  (the "License"); you may not use this file
+ * except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.openairinterface.org/?page_id=698
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *-------------------------------------------------------------------------------
+ * For more information about the OpenAirInterface (OAI) Software Alliance:
+ *      contact@openairinterface.org
+ */
+
+#ifndef __OAI_CUDA_H__
+#define __OAI_CUDA_H__
+
+#include <stdint.h>
+
+#ifdef __NVCC__
+typedef struct complex16 {
+  int16_t r;
+  int16_t i;
+} c16_t;
+#else
+#include "PHY/TOOLS/tools_defs.h"
+#endif
+
+#ifdef __NVCC__
+#include <curand_kernel.h>
+__device__ float2 complex_mul(float2 a, float2 b);
+
+__global__ void multipath_channel_kernel(const float2 *__restrict__ d_channel_coeffs,
+                                         const float *__restrict__ tx_sig,
+                                         float2 *__restrict__ rx_sig,
+                                         int num_samples,
+                                         int channel_length,
+                                         int nb_tx,
+                                         int nb_rx);
+
+__global__ void add_noise_and_phase_noise_kernel(const float2 *__restrict__ r_sig,
+                                                 short2 *__restrict__ output_sig,
+                                                 curandState_t *states,
+                                                 int num_samples,
+                                                 float sigma,
+                                                 float pn_std_dev,
+                                                 bool apply_phase_noise);
+
+#endif // __NVCC__
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+void init_cuda_chsim_buffers(int use_cuda,
+                             int n_tx,
+                             int n_rx,
+                             void **d_tx_sig_p,
+                             void **d_intermediate_sig_p,
+                             void **d_final_output_p,
+                             void **d_curand_states_p,
+                             void **h_tx_sig_pinned_p,
+                             void **h_final_output_pinned_p,
+                             void **d_channel_coeffs_gpu_p);
+
+void free_cuda_chsim_buffers(int use_cuda,
+                             void **d_tx_sig_p,
+                             void **d_intermediate_sig_p,
+                             void **d_final_output_p,
+                             void **d_curand_states_p,
+                             void **h_tx_sig_pinned_p,
+                             void **h_final_output_pinned_p,
+                             float **h_channel_coeffs_p,
+                             void **d_channel_coeffs_gpu_p);
+
+void multipath_channel_cuda(float **rx_sig_re,
+                            float **rx_sig_im,
+                            int nb_tx,
+                            int nb_rx,
+                            int channel_length,
+                            uint32_t length,
+                            uint64_t channel_offset,
+                            float *h_channel_coeffs,
+                            void *d_tx_sig,
+                            void *d_rx_sig,
+                            void *d_channel_coeffs,
+                            void *h_tx_sig_pinned);
+
+void add_noise_cuda(const float **r_re,
+                    const float **r_im,
+                    c16_t **output_signal,
+                    int num_samples,
+                    int nb_rx,
+                    float sigma2,
+                    double ts,
+                    int slot_offset,
+                    int delay,
+                    uint16_t pdu_bit_map,
+                    uint16_t ptrs_bit_map,
+                    void *d_r_sig,
+                    void *d_output_sig,
+                    void *d_curand_states,
+                    void *h_r_sig_pinned,
+                    void *h_output_sig_pinned);
+
+void run_channel_pipeline_cuda(c16_t **output_signal,
+                               int nb_tx,
+                               int nb_rx,
+                               int channel_length,
+                               uint32_t num_samples,
+                               float *h_channel_coeffs,
+                               float sigma2,
+                               double ts,
+                               uint16_t pdu_bit_map,
+                               uint16_t ptrs_bit_map,
+                               int slot_offset,
+                               int delay,
+                               void *d_tx_sig,
+                               void *d_intermediate_sig,
+                               void *d_final_output,
+                               void *d_curand_states,
+                               void *h_tx_sig_pinned,
+                               void *h_final_output_pinned,
+                               void *d_channel_coeffs);
+
+void run_channel_pipeline_cuda_batched(int num_channels,
+                                       int nb_tx,
+                                       int nb_rx,
+                                       int channel_length,
+                                       uint32_t num_samples,
+                                       void *d_channel_coeffs_batch,
+                                       float sigma2,
+                                       double ts,
+                                       uint16_t pdu_bit_map,
+                                       uint16_t ptrs_bit_map,
+                                       void *d_tx_sig_batch,
+                                       void *d_intermediate_sig_batch,
+                                       void *d_final_output_batch,
+                                       void *d_curand_states);
+
+void run_channel_pipeline_cuda_streamed(int nb_tx,
+                                        int nb_rx,
+                                        int channel_length,
+                                        uint32_t num_samples,
+                                        float *h_channel_coeffs,
+                                        float sigma2,
+                                        double ts,
+                                        uint16_t pdu_bit_map,
+                                        uint16_t ptrs_bit_map,
+                                        void *d_tx_sig_void,
+                                        void *d_intermediate_sig_void,
+                                        void *d_final_output_void,
+                                        void *d_curand_states_void,
+                                        void *h_tx_sig_pinned_void,
+                                        void *d_channel_coeffs_void,
+                                        void *stream_void);
+
+void sum_channel_outputs_cuda(void **d_individual_outputs, void *d_final_output, int num_channels, int nb_rx, int num_samples);
+
+/**
+ * \brief Interleaves separate real and imaginary signal buffers into a complex format.
+ *
+ * This utility function takes separate planar buffers for the real (I) and imaginary (Q)
+ * components of a signal and combines them into a single interleaved buffer of float2
+ * structs, which is a common format for CUDA processing.
+ *
+ * \param[in]  rx_sig_re          Array of pointers to the real parts of the input signals.
+ * \param[in]  rx_sig_im          Array of pointers to the imaginary parts of the input signals.
+ * \param[out] output_interleaved Pointer to an array of pointers where the interleaved output will be stored.
+ * \param[in]  nb_rx              The number of receive antennas (i.e., the number of signals).
+ * \param[in]  num_samples        The number of samples per signal.
+ *
+ * \note The caller is responsible for allocating memory for the `output_interleaved` buffer. It should
+ * point to an array of `nb_rx` pointers, where each of those pointers is allocated with a size of
+ * `num_samples * sizeof(float2)`.
+ */
+void interleave_channel_output_cuda(float **rx_sig_re, float **rx_sig_im, void **output_interleaved, int nb_rx, int num_samples);
+
+void *create_and_init_curand_states_cuda(int num_elements, unsigned long long seed);
+void destroy_curand_states_cuda(void *d_curand_states);
+
+#ifdef __cplusplus
+}
+#endif
+
+#endif // __OAI_CUDA_H__

openair1/SIMULATION/TOOLS/phase_noise.c

@@ -22,12 +22,11 @@
 #include <math.h>
 #include "sim.h"
 
-
 /* linear phase noise model */
 void phase_noise(double ts, int16_t *InRe, int16_t *InIm)
 {
-  static uint64_t i=0;
-  double fd = 300;//0.01*30000
+  static uint64_t i = 0;
+  double fd = 300; // 0.01*30000
   double phase = (double)(i * fd * ts);
   c16_t rot = get_sin_cos(phase);
   c16_t val = {.r = *InRe, .i = *InIm};
@@ -61,3 +60,29 @@ void add_noise(c16_t **rxdata,
     }
   }
 }
+
+void add_noise_float(c16_t **rxdata,
+                     const float **r_re,
+                     const float **r_im,
+                     const float sigma,
+                     const int length,
+                     const int slot_offset,
+                     const double ts,
+                     const int delay,
+                     bool apply_phase_noise,
+                     const uint8_t nb_antennas_rx)
+{
+  float sqrt_sigma_div2 = sqrtf(sigma / 2.0f);
+
+  for (int i = 0; i < length; i++) {
+    for (int ap = 0; ap < nb_antennas_rx; ap++) {
+      c16_t *rxd = &rxdata[ap][slot_offset + i + delay];
+      rxd->r = (int16_t)(r_re[ap][i] + (gaussZiggurat(0.0, 1.0) * sqrt_sigma_div2));
+      rxd->i = (int16_t)(r_im[ap][i] + (gaussZiggurat(0.0, 1.0) * sqrt_sigma_div2));
+
+      if (apply_phase_noise) {
+        phase_noise(ts, &rxd->r, &rxd->i);
+      }
+    }
+  }
+}

openair1/SIMULATION/TOOLS/phase_noise.cu

@@ -0,0 +1,234 @@
+/*
+ * Licensed to the OpenAirInterface (OAI) Software Alliance under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The OpenAirInterface Software Alliance licenses this file to You under
+ * the OAI Public License, Version 1.1  (the "License"); you may not use this file
+ * except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.openairinterface.org/?page_id=698
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *-------------------------------------------------------------------------------
+ * For more information about the OpenAirInterface (OAI) Software Alliance:
+ *      contact@openairinterface.org
+ */
+
+#include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
+#include <cuda_runtime.h>
+#include <curand_kernel.h>
+#include "oai_cuda.h"
+
+#define CHECK_CUDA(val) checkCuda((val), __FILE__, __LINE__)
+static void checkCuda(cudaError_t result, const char *file, int line)
+{
+  if (result != cudaSuccess) {
+    fprintf(stderr, "CUDA Error at %s:%d: %s\n", file, line, cudaGetErrorString(result));
+    exit(1);
+  }
+}
+
+__device__ __forceinline__ float2 complex_mul(float2 a, float2 b)
+{
+  return make_float2(a.x * b.x - a.y * b.y, a.x * b.y + a.y * b.x);
+}
+
+__global__ void init_curand_states_kernel(curandState_t *states, unsigned long long seed, int num_elements)
+{
+  int idx = blockIdx.x * blockDim.x + threadIdx.x;
+  if (idx < num_elements) {
+    curand_init(seed, idx, 0, &states[idx]);
+  }
+}
+
+__global__ void add_noise_and_phase_noise_kernel(const float2 *__restrict__ r_sig,
+                                                 short2 *__restrict__ output_sig,
+                                                 curandState_t *states,
+                                                 int num_samples,
+                                                 float sigma,
+                                                 float pn_std_dev,
+                                                 bool apply_phase_noise)
+{
+  const int i = blockIdx.x * blockDim.x + threadIdx.x;
+  const int ii = blockIdx.y;
+
+  if (i >= num_samples)
+    return;
+
+  // Each thread handles one sample and its corresponding cuRAND state
+  curandState_t local_state = states[ii * num_samples + i];
+
+  float2 noisy_signal = r_sig[ii * num_samples + i];
+
+  float2 awgn = curand_normal2(&local_state);
+  noisy_signal.x += awgn.x * sigma;
+  noisy_signal.y += awgn.y * sigma;
+
+  float2 final_signal = noisy_signal;
+
+  if (apply_phase_noise) {
+    float phase_error = curand_normal(&local_state) * pn_std_dev;
+    float cos_phi, sin_phi;
+    __sincosf(phase_error, &sin_phi, &cos_phi);
+    float2 phase_rot = make_float2(cos_phi, sin_phi);
+    final_signal = complex_mul(noisy_signal, phase_rot);
+  }
+
+  states[ii * num_samples + i] = local_state;
+  output_sig[ii * num_samples + i] = make_short2((short)fmaxf(-32768.0f, fminf(32767.0f, final_signal.x)),
+                                                 (short)fmaxf(-32768.0f, fminf(32767.0f, final_signal.y)));
+}
+
+__global__ void add_noise_and_phase_noise_kernel_batched(const float2 *__restrict__ r_sig,
+                                                         short2 *__restrict__ output_sig,
+                                                         curandState_t *states,
+                                                         int num_samples,
+                                                         int nb_rx,
+                                                         float sigma,
+                                                         float pn_std_dev,
+                                                         bool apply_phase_noise)
+{
+  const int i = blockIdx.x * blockDim.x + threadIdx.x; // Sample index
+  const int ii = blockIdx.y; // RX antenna index
+  const int c = blockIdx.z; // Channel index
+
+  if (i >= num_samples)
+    return;
+
+  const int flat_output_idx = (c * nb_rx * num_samples) + (ii * num_samples) + i;
+  // Index for the smaller, reused pool of cuRAND states (does NOT include channel)
+  const int state_pool_idx = (ii * num_samples) + i;
+
+  curandState_t local_state = states[state_pool_idx];
+
+  float2 noisy_signal = r_sig[flat_output_idx];
+
+  float2 awgn = curand_normal2(&local_state);
+  noisy_signal.x += awgn.x * sigma;
+  noisy_signal.y += awgn.y * sigma;
+
+  float2 final_signal = noisy_signal;
+
+  if (apply_phase_noise) {
+    float phase_error = curand_normal(&local_state) * pn_std_dev;
+    float cos_phi, sin_phi;
+    __sincosf(phase_error, &sin_phi, &cos_phi);
+    float2 phase_rot = make_float2(cos_phi, sin_phi);
+    final_signal = complex_mul(noisy_signal, phase_rot);
+  }
+
+  states[state_pool_idx] = local_state;
+
+  output_sig[flat_output_idx] = make_short2((short)fmaxf(-32768.0f, fminf(32767.0f, final_signal.x)),
+                                            (short)fmaxf(-32768.0f, fminf(32767.0f, final_signal.y)));
+}
+
+extern "C" {
+
+void add_noise_cuda(const float **r_re,
+                    const float **r_im,
+                    c16_t **output_signal,
+                    int num_samples,
+                    int nb_rx,
+                    float sigma2,
+                    double ts,
+                    int slot_offset,
+                    int delay,
+                    uint16_t pdu_bit_map,
+                    uint16_t ptrs_bit_map,
+                    void *d_r_sig_void,
+                    void *d_output_sig_void,
+                    void *d_curand_states_void,
+                    void *h_r_sig_void,
+                    void *h_output_temp_void)
+{
+  float2 *d_r_sig = (float2 *)d_r_sig_void;
+  short2 *d_output_sig = (short2 *)d_output_sig_void;
+  curandState_t *d_curand_states = (curandState_t *)d_curand_states_void;
+
+  float2 *kernel_input_ptr;
+
+#if defined(USE_UNIFIED_MEMORY)
+  for (int ii = 0; ii < nb_rx; ii++) {
+    for (int i = 0; i < num_samples; i++) {
+      d_r_sig[ii * num_samples + i] = make_float2(r_re[ii][i], r_im[ii][i]);
+    }
+  }
+  kernel_input_ptr = d_r_sig;
+#elif defined(USE_ATS_MEMORY)
+  float2 *h_r_sig = (float2 *)h_r_sig_void;
+  for (int ii = 0; ii < nb_rx; ii++) {
+    for (int i = 0; i < num_samples; i++) {
+      h_r_sig[ii * num_samples + i] = make_float2(r_re[ii][i], r_im[ii][i]);
+    }
+  }
+  kernel_input_ptr = h_r_sig;
+#else // EXPLICIT COPY
+  float2 *h_r_sig = (float2 *)h_r_sig_void;
+  for (int ii = 0; ii < nb_rx; ii++) {
+    for (int i = 0; i < num_samples; i++) {
+      h_r_sig[ii * num_samples + i] = make_float2(r_re[ii][i], r_im[ii][i]);
+    }
+  }
+  CHECK_CUDA(cudaMemcpy(d_r_sig, h_r_sig, nb_rx * num_samples * sizeof(float2), cudaMemcpyHostToDevice));
+  kernel_input_ptr = d_r_sig;
+#endif
+
+  dim3 threadsPerBlock(256, 1);
+  dim3 numBlocks((num_samples + threadsPerBlock.x - 1) / threadsPerBlock.x, nb_rx);
+  float pn_variance = 1e-5f * 2.0f * 3.1415926535f * 300.0f * (float)ts;
+  bool apply_phase_noise = (pdu_bit_map & ptrs_bit_map);
+  add_noise_and_phase_noise_kernel<<<numBlocks, threadsPerBlock>>>(kernel_input_ptr,
+                                                                   d_output_sig,
+                                                                   d_curand_states,
+                                                                   num_samples,
+                                                                   sqrtf(sigma2 / 2.0f),
+                                                                   sqrtf(pn_variance),
+                                                                   apply_phase_noise);
+
+#if defined(USE_UNIFIED_MEMORY)
+  CHECK_CUDA(cudaDeviceSynchronize());
+  for (int ii = 0; ii < nb_rx; ii++) {
+    for (int i = 0; i < num_samples; i++) {
+      short2 result = d_output_sig[ii * num_samples + i];
+      output_signal[ii][i + slot_offset + delay].r = result.x;
+      output_signal[ii][i + slot_offset + delay].i = result.y;
+    }
+  }
+#else
+  short2 *h_output_temp = (short2 *)h_output_temp_void;
+  CHECK_CUDA(cudaMemcpy(h_output_temp, d_output_sig, nb_rx * num_samples * sizeof(short2), cudaMemcpyDeviceToHost));
+  CHECK_CUDA(cudaDeviceSynchronize());
+  for (int ii = 0; ii < nb_rx; ii++) {
+    memcpy(output_signal[ii] + slot_offset + delay, h_output_temp + ii * num_samples, num_samples * sizeof(short2));
+  }
+#endif
+}
+
+void *create_and_init_curand_states_cuda(int num_elements, unsigned long long seed)
+{
+  void *d_states_void;
+  CHECK_CUDA(cudaMalloc(&d_states_void, num_elements * sizeof(curandState_t)));
+
+  int threads = 256;
+  int blocks = (num_elements + threads - 1) / threads;
+  init_curand_states_kernel<<<blocks, threads>>>((curandState_t *)d_states_void, seed, num_elements);
+  CHECK_CUDA(cudaDeviceSynchronize());
+
+  return d_states_void;
+}
+
+void destroy_curand_states_cuda(void *d_curand_states)
+{
+  if (d_curand_states) {
+    CHECK_CUDA(cudaFree(d_curand_states));
+  }
+}
+} // extern "C"

openair1/SIMULATION/TOOLS/sim.h

@@ -412,13 +412,39 @@ int random_channel(channel_desc_t *desc, uint8_t abstraction_flag);
 */
 
 void multipath_channel(channel_desc_t *desc,
-                       double *tx_sig_re[NB_ANTENNAS_TX],
-                       double *tx_sig_im[NB_ANTENNAS_TX],
-                       double *rx_sig_re[NB_ANTENNAS_RX],
-                       double *rx_sig_im[NB_ANTENNAS_RX],
+                       double **tx_sig_re,
+                       double **tx_sig_im,
+                       double **rx_sig_re,
+                       double **rx_sig_im,
                        uint32_t length,
                        uint8_t keep_channel,
 		       int log_channel);
+
+void multipath_channel_float(channel_desc_t *desc,
+                             float **tx_sig_interleaved,
+                             float **rx_sig_re,
+                             float **rx_sig_im,
+                             uint32_t length,
+                             uint8_t keep_channel,
+                             int log_channel);
+
+void interleave_channel_output(float **rx_sig_re,
+                               float **rx_sig_im,
+                               float **output_interleaved,
+                               int nb_rx,
+                               int num_samples);
+
+void add_noise_float(c16_t **rxdata,
+                     const float **r_re,
+                     const float **r_im,
+                     const float sigma2,
+                     const int length,
+                     const int slot_offset,
+                     const double ts,
+                     const int delay,
+                     bool apply_phase_noise,
+                     const uint8_t nb_antennas_rx);
+
 /*
 \fn double compute_pbch_sinr(channel_desc_t *desc,
                              channel_desc_t *desc_i1,