Files
openairinterface5g/openair1/SIMULATION/TOOLS/channel_pipeline.c
T
Bartosz Podrygajlo 47ba5d3ca5 Acceleration of channel convolution and noise generation
Added two versions of channel convolution and noise generation:
 - accelerated via threadpool
 - accelerated using CUDA

The main difference between this and previous versions of channel
convolution implementations is that these functions take a real-world
approach to input/output where both could be split unevenly over a set of
buffers, e.g. ring buffers used in vrtsim.

The CUDA-accelerated version only works on systems with unified memory, e.g. NVidia
DGX Spark or GH
2026-03-30 09:48:50 +02:00

186 lines
6.1 KiB
C

/*
* SPDX-License-Identifier: LicenseRef-CSSL-1.0
*/
#include <math.h>
#include "utils.h"
#include "channel_pipeline.h"
#include "task_ans.h"
#include "noise_device.h"
#include "thread-pool.h"
typedef struct {
const c16_t **tx_sig0;
const c16_t **tx_sig1;
int num_samples_tx_sig0;
int nb_tx;
int num_samples_rx_sig0;
int num_samples;
int channel_length;
int num_jobs;
task_ans_t *task_ans;
float noise_power;
} job_common_args_t;
typedef struct {
job_common_args_t *common;
const cf_t **channel;
c16_t *rx_sig0;
c16_t *rx_sig1;
int job_index;
} job_args_t;
void do_convolution_and_noise(int nb_tx,
int channel_length,
const cf_t **channel,
const c16_t **tx_sig0,
const c16_t **tx_sig1,
int num_samples_tx_sig0,
c16_t *rx_sig0,
c16_t *rx_sig1,
int num_samples_rx_sig0,
int num_samples,
int job_index,
int num_jobs,
float noise_power)
{
const int batch_size = 4000;
cf_t work_buffer[batch_size] __attribute__((aligned(64)));
for (int batch_start = job_index * batch_size; batch_start < num_samples; batch_start += batch_size * num_jobs) {
int batch_end = min(batch_start + batch_size, num_samples);
if (noise_power > 0.0f) {
get_noise_vector((float *)work_buffer, batch_size * 2);
}
for (int i = batch_start; i < batch_end; i++) {
float rx_r = 0.0f;
float rx_i = 0.0f;
for (int tx_ant = 0; tx_ant < nb_tx; tx_ant++) {
for (int l = 0; l < channel_length; l++) {
int idx = i + (channel_length - 1) - l;
c16_t in_sample;
if (idx < num_samples_tx_sig0) {
in_sample = tx_sig0[tx_ant][idx];
} else {
in_sample = tx_sig1[tx_ant][idx - num_samples_tx_sig0];
}
cf_t tx_sample = {(float)in_sample.r, (float)in_sample.i};
cf_t ch = channel[tx_ant][l];
rx_r += tx_sample.r * ch.r - tx_sample.i * ch.i;
rx_i += tx_sample.r * ch.i + tx_sample.i * ch.r;
}
}
if (noise_power > 0.0f) {
work_buffer[i - batch_start].r += rx_r;
work_buffer[i - batch_start].i += rx_i;
} else {
work_buffer[i - batch_start].r = rx_r;
work_buffer[i - batch_start].i = rx_i;
}
}
for (int i = batch_start; i < batch_end; i++) {
if (i < num_samples_rx_sig0) {
rx_sig0[i].r = work_buffer[i - batch_start].r;
rx_sig0[i].i = work_buffer[i - batch_start].i;
} else {
rx_sig1[i - num_samples_rx_sig0].r = work_buffer[i - batch_start].r;
rx_sig1[i - num_samples_rx_sig0].i = work_buffer[i - batch_start].i;
}
}
}
}
void channel_job(void *args)
{
job_args_t *job_args = (job_args_t *)args;
job_common_args_t *common = job_args->common;
do_convolution_and_noise(common->nb_tx,
common->channel_length,
job_args->channel,
common->tx_sig0,
common->tx_sig1,
common->num_samples_tx_sig0,
job_args->rx_sig0,
job_args->rx_sig1,
common->num_samples_rx_sig0,
common->num_samples,
job_args->job_index,
common->num_jobs,
common->noise_power);
completed_task_ans(common->task_ans);
}
void channel_pipeline(void *tpool,
const cf_t **channel,
const c16_t **tx_sig0,
const c16_t **tx_sig1,
int num_samples_tx_sig0,
c16_t **rx_sig0,
c16_t **rx_sig1,
int num_samples_rx_sig0,
int num_samples,
int channel_length,
int nb_tx,
int nb_rx,
float noise_power)
{
AssertFatal(nb_tx > 0, "nb_tx must be positive (%d)\n", nb_tx);
AssertFatal(nb_rx > 0, "nb_rx must be positive (%d)\n", nb_rx);
AssertFatal(nb_tx <= 64, "Number of TX antennas is too large (%d)\n", nb_tx);
AssertFatal(nb_rx <= 64, "Number of RX antennas is too large (%d)\n", nb_rx);
tpool_t *thread_pool = (tpool_t *)tpool;
size_t num_tpool_threads = thread_pool->len_thr;
job_common_args_t common_args;
common_args.tx_sig0 = tx_sig0;
if (tx_sig1) {
common_args.tx_sig1 = tx_sig1;
} else {
common_args.tx_sig1 = NULL;
}
// At least 1 job per RX antenna. Attempt to saturate the threadpool
size_t num_jobs_per_rx_antenna = (num_tpool_threads + nb_rx - 1) / nb_rx;
common_args.num_samples_tx_sig0 = num_samples_tx_sig0;
common_args.num_samples_rx_sig0 = num_samples_rx_sig0;
common_args.num_samples = num_samples;
common_args.channel_length = channel_length;
common_args.nb_tx = nb_tx;
common_args.num_jobs = num_jobs_per_rx_antenna;
common_args.noise_power = noise_power;
task_ans_t task_ans;
init_task_ans(&task_ans, nb_rx * num_jobs_per_rx_antenna);
common_args.task_ans = &task_ans;
job_args_t job_args_array[nb_rx][num_jobs_per_rx_antenna];
for (int aarx = 0; aarx < nb_rx; aarx++) {
for (int job_idx = 0; job_idx < num_jobs_per_rx_antenna; job_idx++) {
job_args_t *job_args = &job_args_array[aarx][job_idx];
job_args->common = &common_args;
job_args->channel = &channel[nb_tx * aarx];
job_args->rx_sig0 = rx_sig0[aarx];
if (rx_sig1) {
job_args->rx_sig1 = rx_sig1[aarx];
} else {
job_args->rx_sig1 = NULL;
}
job_args->job_index = job_idx;
task_t task;
task.args = job_args;
task.func = channel_job;
pushTpool(tpool, task);
}
}
join_task_ans(&task_ans);
}
void channel_pipeline_init(float noise_power) {
init_noise_device(noise_power);
}
void channel_pipeline_shutdown(void) {
free_noise_device();
}