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gnss-sdr/src/algorithms/tracking/libs/cuda_multicorrelator.cu
Javier 6cdca3b269 cuda multicorrelator bug correction
2016-06-17 17:35:19 +02:00

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/*!
* \file cuda_multicorrelator.cu
* \brief High optimized CUDA GPU vector multiTAP correlator class
* \authors <ul>
* <li> Javier Arribas, 2015. jarribas(at)cttc.es
* </ul>
*
* Class that implements a high optimized vector multiTAP correlator class for NVIDIA CUDA GPUs
*
* -------------------------------------------------------------------------
*
* Copyright (C) 2010-2015 (see AUTHORS file for a list of contributors)
*
* GNSS-SDR is a software defined Global Navigation
* Satellite Systems receiver
*
* This file is part of GNSS-SDR.
*
* GNSS-SDR is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* GNSS-SDR is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with GNSS-SDR. If not, see <http://www.gnu.org/licenses/>.
*
* -------------------------------------------------------------------------
*/
#include "cuda_multicorrelator.h"
#include <stdio.h>
#include <iostream>
// For the CUDA runtime routines (prefixed with "cuda_")
#include <cuda_runtime.h>
#define ACCUM_N 128
__global__ void Doppler_wippe_scalarProdGPUCPXxN_shifts_chips(
GPU_Complex *d_corr_out,
GPU_Complex *d_sig_in,
GPU_Complex *d_sig_wiped,
GPU_Complex *d_local_code_in,
float *d_shifts_chips,
int code_length_chips,
float code_phase_step_chips,
float rem_code_phase_chips,
int vectorN,
int elementN,
float rem_carrier_phase_in_rad,
float phase_step_rad
)
{
//Accumulators cache
__shared__ GPU_Complex accumResult[ACCUM_N];
// CUDA version of floating point NCO and vector dot product integrated
float sin;
float cos;
for (int i = blockIdx.x * blockDim.x + threadIdx.x;
i < elementN;
i += blockDim.x * gridDim.x)
{
__sincosf(rem_carrier_phase_in_rad + i*phase_step_rad, &sin, &cos);
d_sig_wiped[i] = d_sig_in[i] * GPU_Complex(cos,-sin);
}
__syncthreads();
////////////////////////////////////////////////////////////////////////////
// Cycle through every pair of vectors,
// taking into account that vector counts can be different
// from total number of thread blocks
////////////////////////////////////////////////////////////////////////////
for (int vec = blockIdx.x; vec < vectorN; vec += gridDim.x)
{
//int vectorBase = IMUL(elementN, vec);
//int vectorEnd = elementN;
////////////////////////////////////////////////////////////////////////
// Each accumulator cycles through vectors with
// stride equal to number of total number of accumulators ACCUM_N
// At this stage ACCUM_N is only preferred be a multiple of warp size
// to meet memory coalescing alignment constraints.
////////////////////////////////////////////////////////////////////////
for (int iAccum = threadIdx.x; iAccum < ACCUM_N; iAccum += blockDim.x)
{
GPU_Complex sum = GPU_Complex(0,0);
float local_code_chip_index=0.0;;
//float code_phase;
for (int pos = iAccum; pos < elementN; pos += ACCUM_N)
{
//original sample code
//sum = sum + d_sig_in[pos-vectorBase] * d_nco_in[pos-vectorBase] * d_local_codes_in[pos];
//sum = sum + d_sig_in[pos-vectorBase] * d_local_codes_in[pos];
//sum.multiply_acc(d_sig_in[pos],d_local_codes_in[pos+d_shifts_samples[vec]]);
//custom code for multitap correlator
// 1.resample local code for the current shift
local_code_chip_index= fmodf(code_phase_step_chips*__int2float_rd(pos)+ d_shifts_chips[vec] - rem_code_phase_chips, code_length_chips);
//Take into account that in multitap correlators, the shifts can be negative!
if (local_code_chip_index<0.0) local_code_chip_index+=(code_length_chips-1);
//printf("vec= %i, pos %i, chip_idx=%i chip_shift=%f \r\n",vec, pos,__float2int_rd(local_code_chip_index),local_code_chip_index);
// 2.correlate
sum.multiply_acc(d_sig_wiped[pos],d_local_code_in[__float2int_rd(local_code_chip_index)]);
}
accumResult[iAccum] = sum;
}
////////////////////////////////////////////////////////////////////////
// Perform tree-like reduction of accumulators' results.
// ACCUM_N has to be power of two at this stage
////////////////////////////////////////////////////////////////////////
for (int stride = ACCUM_N / 2; stride > 0; stride >>= 1)
{
__syncthreads();
for (int iAccum = threadIdx.x; iAccum < stride; iAccum += blockDim.x)
{
accumResult[iAccum] += accumResult[stride + iAccum];
}
}
if (threadIdx.x == 0)
{
d_corr_out[vec] = accumResult[0];
}
}
}
bool cuda_multicorrelator::init_cuda_integrated_resampler(
int signal_length_samples,
int code_length_chips,
int n_correlators
)
{
// use command-line specified CUDA device, otherwise use device with highest Gflops/s
// findCudaDevice(argc, (const char **)argv);
cudaDeviceProp prop;
int num_devices, device;
cudaGetDeviceCount(&num_devices);
if (num_devices > 1) {
int max_multiprocessors = 0, max_device = 0;
for (device = 0; device < num_devices; device++) {
cudaDeviceProp properties;
cudaGetDeviceProperties(&properties, device);
if (max_multiprocessors < properties.multiProcessorCount) {
max_multiprocessors = properties.multiProcessorCount;
max_device = device;
}
printf("Found GPU device # %i\n",device);
}
//cudaSetDevice(max_device);
//set random device!
selected_gps_device=rand() % num_devices;//generates a random number between 0 and num_devices to split the threads between GPUs
cudaSetDevice(selected_gps_device);
cudaGetDeviceProperties( &prop, max_device );
//debug code
if (prop.canMapHostMemory != 1) {
printf( "Device can not map memory.\n" );
}
printf("L2 Cache size= %u \n",prop.l2CacheSize);
printf("maxThreadsPerBlock= %u \n",prop.maxThreadsPerBlock);
printf("maxGridSize= %i \n",prop.maxGridSize[0]);
printf("sharedMemPerBlock= %lu \n",prop.sharedMemPerBlock);
printf("deviceOverlap= %i \n",prop.deviceOverlap);
printf("multiProcessorCount= %i \n",prop.multiProcessorCount);
}else{
cudaGetDevice( &selected_gps_device);
cudaGetDeviceProperties( &prop, selected_gps_device );
//debug code
if (prop.canMapHostMemory != 1) {
printf( "Device can not map memory.\n" );
}
printf("L2 Cache size= %u \n",prop.l2CacheSize);
printf("maxThreadsPerBlock= %u \n",prop.maxThreadsPerBlock);
printf("maxGridSize= %i \n",prop.maxGridSize[0]);
printf("sharedMemPerBlock= %lu \n",prop.sharedMemPerBlock);
printf("deviceOverlap= %i \n",prop.deviceOverlap);
printf("multiProcessorCount= %i \n",prop.multiProcessorCount);
}
// (cudaFuncSetCacheConfig(CUDA_32fc_x2_multiply_x2_dot_prod_32fc_, cudaFuncCachePreferShared));
// ALLOCATE GPU MEMORY FOR INPUT/OUTPUT and INTERNAL vectors
size_t size = signal_length_samples * sizeof(GPU_Complex);
//********* ZERO COPY VERSION ************
// Set flag to enable zero copy access
// Optimal in shared memory devices (like Jetson K1)
//cudaSetDeviceFlags(cudaDeviceMapHost);
//******** CudaMalloc version ***********
// input signal GPU memory (can be mapped to CPU memory in shared memory devices!)
// cudaMalloc((void **)&d_sig_in, size);
// cudaMemset(d_sig_in,0,size);
// Doppler-free signal (internal GPU memory)
cudaMalloc((void **)&d_sig_doppler_wiped, size);
cudaMemset(d_sig_doppler_wiped,0,size);
// Local code GPU memory (can be mapped to CPU memory in shared memory devices!)
cudaMalloc((void **)&d_local_codes_in, sizeof(std::complex<float>)*code_length_chips);
cudaMemset(d_local_codes_in,0,sizeof(std::complex<float>)*code_length_chips);
d_code_length_chips=code_length_chips;
// Vector with the chip shifts for each correlator tap
//GPU memory (can be mapped to CPU memory in shared memory devices!)
cudaMalloc((void **)&d_shifts_chips, sizeof(float)*n_correlators);
cudaMemset(d_shifts_chips,0,sizeof(float)*n_correlators);
//scalars
//cudaMalloc((void **)&d_corr_out, sizeof(std::complex<float>)*n_correlators);
//cudaMemset(d_corr_out,0,sizeof(std::complex<float>)*n_correlators);
// Launch the Vector Add CUDA Kernel
// TODO: write a smart load balance using device info!
threadsPerBlock = 64;
blocksPerGrid = 128;//(int)(signal_length_samples+threadsPerBlock-1)/threadsPerBlock;
cudaStreamCreate (&stream1) ;
//cudaStreamCreate (&stream2) ;
return true;
}
bool cuda_multicorrelator::set_local_code_and_taps(
int code_length_chips,
const std::complex<float>* local_codes_in,
float *shifts_chips,
int n_correlators
)
{
cudaSetDevice(selected_gps_device);
//********* ZERO COPY VERSION ************
// // Get device pointer from host memory. No allocation or memcpy
// cudaError_t code;
// // local code CPU -> GPU copy memory
// code=cudaHostGetDevicePointer((void **)&d_local_codes_in, (void *) local_codes_in, 0);
// if (code!=cudaSuccess)
// {
// printf("cuda cudaHostGetDevicePointer error in set_local_code_and_taps \r\n");
// }
// // Correlator shifts vector CPU -> GPU copy memory (fractional chip shifts are allowed!)
// code=cudaHostGetDevicePointer((void **)&d_shifts_chips, (void *) shifts_chips, 0);
// if (code!=cudaSuccess)
// {
// printf("cuda cudaHostGetDevicePointer error in set_local_code_and_taps \r\n");
// }
//******** CudaMalloc version ***********
//local code CPU -> GPU copy memory
cudaMemcpyAsync(d_local_codes_in, local_codes_in, sizeof(GPU_Complex)*code_length_chips, cudaMemcpyHostToDevice,stream1);
d_code_length_chips=code_length_chips;
//Correlator shifts vector CPU -> GPU copy memory (fractional chip shifts are allowed!)
cudaMemcpyAsync(d_shifts_chips, shifts_chips, sizeof(float)*n_correlators,
cudaMemcpyHostToDevice,stream1);
return true;
}
bool cuda_multicorrelator::set_input_output_vectors(
std::complex<float>* corr_out,
std::complex<float>* sig_in
)
{
cudaSetDevice(selected_gps_device);
// Save CPU pointers
d_sig_in_cpu =sig_in;
d_corr_out_cpu = corr_out;
// Zero Copy version
// Get device pointer from host memory. No allocation or memcpy
cudaError_t code;
code=cudaHostGetDevicePointer((void **)&d_sig_in, (void *) sig_in, 0);
code=cudaHostGetDevicePointer((void **)&d_corr_out, (void *) corr_out, 0);
if (code!=cudaSuccess)
{
printf("cuda cudaHostGetDevicePointer error \r\n");
}
return true;
}
#define gpuErrchk(ans) { gpuAssert((ans), __FILE__, __LINE__); }
inline void gpuAssert(cudaError_t code, const char *file, int line, bool abort=true)
{
if (code != cudaSuccess)
{
fprintf(stderr,"GPUassert: %s %s %d\n", cudaGetErrorString(code), file, line);
if (abort) exit(code);
}
}
bool cuda_multicorrelator::Carrier_wipeoff_multicorrelator_resampler_cuda(
float rem_carrier_phase_in_rad,
float phase_step_rad,
float code_phase_step_chips,
float rem_code_phase_chips,
int signal_length_samples,
int n_correlators)
{
cudaSetDevice(selected_gps_device);
// cudaMemCpy version
//size_t memSize = signal_length_samples * sizeof(std::complex<float>);
// input signal CPU -> GPU copy memory
//cudaMemcpyAsync(d_sig_in, d_sig_in_cpu, memSize,
// cudaMemcpyHostToDevice, stream2);
//***** NOTICE: NCO is computed on-the-fly, not need to copy NCO into GPU! ****
//launch the multitap correlator with integrated local code resampler!
Doppler_wippe_scalarProdGPUCPXxN_shifts_chips<<<blocksPerGrid, threadsPerBlock,0 ,stream1>>>(
d_corr_out,
d_sig_in,
d_sig_doppler_wiped,
d_local_codes_in,
d_shifts_chips,
d_code_length_chips,
code_phase_step_chips,
rem_code_phase_chips,
n_correlators,
signal_length_samples,
rem_carrier_phase_in_rad,
phase_step_rad
);
gpuErrchk( cudaPeekAtLastError() );
gpuErrchk( cudaStreamSynchronize(stream1));
// cudaMemCpy version
// Copy the device result vector in device memory to the host result vector
// in host memory.
//scalar products (correlators outputs)
//cudaMemcpyAsync(d_corr_out_cpu, d_corr_out, sizeof(std::complex<float>)*n_correlators,
// cudaMemcpyDeviceToHost,stream1);
return true;
}
cuda_multicorrelator::cuda_multicorrelator()
{
d_sig_in=NULL;
d_nco_in=NULL;
d_sig_doppler_wiped=NULL;
d_local_codes_in=NULL;
d_shifts_samples=NULL;
d_shifts_chips=NULL;
d_corr_out=NULL;
threadsPerBlock=0;
blocksPerGrid=0;
d_code_length_chips=0;
}
bool cuda_multicorrelator::free_cuda()
{
// Free device global memory
if (d_sig_in!=NULL) cudaFree(d_sig_in);
if (d_nco_in!=NULL) cudaFree(d_nco_in);
if (d_sig_doppler_wiped!=NULL) cudaFree(d_sig_doppler_wiped);
if (d_local_codes_in!=NULL) cudaFree(d_local_codes_in);
if (d_corr_out!=NULL) cudaFree(d_corr_out);
if (d_shifts_samples!=NULL) cudaFree(d_shifts_samples);
if (d_shifts_chips!=NULL) cudaFree(d_shifts_chips);
// Reset the device and exit
// cudaDeviceReset causes the driver to clean up all state. While
// not mandatory in normal operation, it is good practice. It is also
// needed to ensure correct operation when the application is being
// profiled. Calling cudaDeviceReset causes all profile data to be
// flushed before the application exits
cudaDeviceReset();
return true;
}
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