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Pinned memory (Zero copy) huge improvement for GPU tracking.

This commit is contained in:
Javier Arribas 2015-10-15 19:09:09 +02:00
parent a4655e2b03
commit 2039e998ff
4 changed files with 202 additions and 417 deletions
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4
conf/gnss-sdr_GPS_L1_gr_complex_gpu.conf
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@ -17,7 +17,7 @@ ControlThread.wait_for_flowgraph=false
SignalSource.implementation=File_Signal_Source
;#filename: path to file with the captured GNSS signal samples to be processed
SignalSource.filename=/home/javier/signals/4msps.dat
SignalSource.filename=/media/javier/SISTEMA/signals/New York/4msps.dat
;#item_type: Type and resolution for each of the signal samples. Use only gr_complex in this version.
SignalSource.item_type=gr_complex
@ -165,7 +165,7 @@ Resampler.sample_freq_out=4000000
;######### CHANNELS GLOBAL CONFIG ############
;#count: Number of available GPS satellite channels.
Channels_GPS.count=1
Channels_GPS.count=8
;#count: Number of available Galileo satellite channels.
Channels_Galileo.count=0
;#in_acquisition: Number of channels simultaneously acquiring for the whole receiver

4
src/algorithms/tracking/gnuradio_blocks/gps_l1_ca_dll_pll_tracking_gpu_cc.h
View File

@ -128,13 +128,9 @@ private:
//GPU HOST PINNED MEMORY IN/OUT VECTORS
gr_complex* in_gpu;
gr_complex* d_carr_sign_gpu;
gr_complex* d_local_codes_gpu;
float* d_local_code_shift_chips;
gr_complex* d_corr_outs_gpu;
cuda_multicorrelator *multicorrelator_gpu;
gr_complex* d_ca_code;
gr_complex *d_Early;

587
src/algorithms/tracking/libs/cuda_multicorrelator.cu
View File

@ -32,26 +32,14 @@
* -------------------------------------------------------------------------
*/
///////////////////////////////////////////////////////////////////////////////
// On G80-class hardware 24-bit multiplication takes 4 clocks per warp
// (the same as for floating point multiplication and addition),
// whereas full 32-bit multiplication takes 16 clocks per warp.
// So if integer multiplication operands are guaranteed to fit into 24 bits
// (always lie withtin [-8M, 8M - 1] range in signed case),
// explicit 24-bit multiplication is preferred for performance.
///////////////////////////////////////////////////////////////////////////////
#define IMUL(a, b) __mul24(a, b)
#include "cuda_multicorrelator.h"
#include <stdio.h>
#include <iostream>
// For the CUDA runtime routines (prefixed with "cuda_")
#include <cuda_runtime.h>
#define ACCUM_N 256
#define ACCUM_N 128
__global__ void scalarProdGPUCPXxN_shifts_chips(
GPU_Complex *d_corr_out,
@ -90,15 +78,17 @@ __global__ void scalarProdGPUCPXxN_shifts_chips(
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
float local_code_chip_index= fmod(code_phase_step_chips*(float)pos + d_shifts_chips[vec] - rem_code_phase_chips, code_length_chips);
//TODO: Take into account that in multitap correlators, the shifts can be negative!
//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;
//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_in[pos],d_local_code_in[__float2int_rd(local_code_chip_index)]);
@ -127,163 +117,6 @@ __global__ void scalarProdGPUCPXxN_shifts_chips(
}
}
///////////////////////////////////////////////////////////////////////////////
// Calculate scalar products of VectorN vectors of ElementN elements on GPU
// Parameters restrictions:
// 1) ElementN is strongly preferred to be a multiple of warp size to
// meet alignment constraints of memory coalescing.
// 2) ACCUM_N must be a power of two.
///////////////////////////////////////////////////////////////////////////////
__global__ void scalarProdGPUCPXxN_shifts(
GPU_Complex *d_corr_out,
GPU_Complex *d_sig_in,
GPU_Complex *d_local_codes_in,
int *d_shifts_samples,
int vectorN,
int elementN
)
{
//Accumulators cache
__shared__ GPU_Complex accumResult[ACCUM_N];
////////////////////////////////////////////////////////////////////////////
// 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 = vectorBase + 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);
for (int pos = vectorBase + iAccum; pos < vectorEnd; pos += ACCUM_N)
{
//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-vectorBase],d_local_codes_in[pos-vectorBase+d_shifts_samples[vec]]);
}
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];
}
}
}
__global__ void scalarProdGPUCPXxN(
GPU_Complex *d_corr_out,
GPU_Complex *d_sig_in,
GPU_Complex *d_local_codes_in,
int vectorN,
int elementN
)
{
//Accumulators cache
__shared__ GPU_Complex accumResult[ACCUM_N];
////////////////////////////////////////////////////////////////////////////
// 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 = vectorBase + 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);
//for (int pos = vectorBase + iAccum; pos < vectorEnd; pos += ACCUM_N)
for (int pos = iAccum; pos < elementN; pos += ACCUM_N)
{
//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-vectorBase],d_local_codes_in[pos]);
sum.multiply_acc(d_sig_in[pos],d_local_codes_in[pos]);
}
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];
}
}
}
//*********** CUDA processing **************
// Treads: a minimal parallel execution code on GPU
// Blocks: a set of N threads
/**
* CUDA Kernel Device code
*
* Computes the vectorial product of A and B into C. The 3 vectors have the same
* number of elements numElements.
*/
__global__ void CUDA_32fc_x2_multiply_32fc( GPU_Complex *A, GPU_Complex *B, GPU_Complex *C, int numElements)
{
for (int i = blockIdx.x * blockDim.x + threadIdx.x;
i < numElements;
i += blockDim.x * gridDim.x)
{
C[i] = A[i] * B[i];
}
}
/**
* CUDA Kernel Device code
*
@ -292,21 +125,7 @@ __global__ void CUDA_32fc_x2_multiply_32fc( GPU_Complex *A, GPU_Complex *B,
__global__ void
CUDA_32fc_Doppler_wipeoff( GPU_Complex *sig_out, GPU_Complex *sig_in, float rem_carrier_phase_in_rad, float phase_step_rad, int numElements)
{
//*** NCO CPU code (GNURadio FXP NCO)
//float sin_f, cos_f;
//float phase_step_rad = static_cast<float>(2 * GALILEO_PI) * d_carrier_doppler_hz / static_cast<float>(d_fs_in);
//int phase_step_rad_i = gr::fxpt::float_to_fixed(phase_step_rad);
//int phase_rad_i = gr::fxpt::float_to_fixed(d_rem_carr_phase_rad);
//
//for(int i = 0; i < d_current_prn_length_samples; i++)
// {
// gr::fxpt::sincos(phase_rad_i, &sin_f, &cos_f);
// d_carr_sign[i] = std::complex<float>(cos_f, -sin_f);
// phase_rad_i += phase_step_rad_i;
// }
// CUDA version of floating point NCO and vector dot product integrated
float sin;
float cos;
for (int i = blockIdx.x * blockDim.x + threadIdx.x;
@ -319,110 +138,101 @@ CUDA_32fc_Doppler_wipeoff( GPU_Complex *sig_out, GPU_Complex *sig_in, float rem
}
/**
* CUDA Kernel Device code
*
* Computes the vectorial product of A and B into C. The 3 vectors have the same
* number of elements numElements.
*/
__global__ void
CUDA_32fc_x2_add_32fc( GPU_Complex *A, GPU_Complex *B, GPU_Complex *C, int numElements)
__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,
float 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 < numElements;
i < elementN;
i += blockDim.x * gridDim.x)
{
C[i] = A[i] + B[i];
__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;
//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;
//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(const int argc, const char **argv, int signal_length_samples, int local_codes_length_samples, 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!
// cudaSetDevice(rand() % num_devices); //generates a random number between 0 and num_devices to split the threads between GPUs
//
// 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{
// int whichDevice;
// cudaGetDevice( &whichDevice );
// cudaGetDeviceProperties( &prop, whichDevice );
// //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);
cudaMalloc((void **)&d_sig_in, size);
// (cudaMalloc((void **)&d_nco_in, size));
cudaMalloc((void **)&d_sig_doppler_wiped, size);
// old version: all local codes are independent vectors
// (cudaMalloc((void **)&d_local_codes_in, size*n_correlators));
// new version: only one vector with extra samples to shift the local code for the correlator set
// Required: The last correlator tap in d_shifts_samples has the largest sample shift
size_t size_local_code_bytes = local_codes_length_samples * sizeof(GPU_Complex);
cudaMalloc((void **)&d_local_codes_in, size_local_code_bytes);
cudaMalloc((void **)&d_shifts_samples, sizeof(int)*n_correlators);
//scalars
cudaMalloc((void **)&d_corr_out, sizeof(std::complex<float>)*n_correlators);
// Launch the Vector Add CUDA Kernel
threadsPerBlock = 256;
blocksPerGrid =(int)(signal_length_samples+threadsPerBlock-1)/threadsPerBlock;
cudaStreamCreate (&stream1) ;
cudaStreamCreate (&stream2) ;
return true;
}
bool cuda_multicorrelator::init_cuda_integrated_resampler(
const int argc, const char **argv,
int signal_length_samples,
int code_length_chips,
int n_correlators
@ -480,34 +290,45 @@ bool cuda_multicorrelator::init_cuda_integrated_resampler(
// (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);
cudaMalloc((void **)&d_sig_in, size);
cudaMemset(d_sig_in,0,size);
//********* ZERO COPY VERSION ************
// Set flag to enable zero copy access
// Optimal in shared memory devices (like Jetson K1)
cudaSetDeviceFlags(cudaDeviceMapHost);
// (cudaMalloc((void **)&d_nco_in, size));
//******** 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);
//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
threadsPerBlock = 256;
// TODO: write a smart load balance using device info!
threadsPerBlock = 64;
blocksPerGrid =(int)(signal_length_samples+threadsPerBlock-1)/threadsPerBlock;
cudaStreamCreate (&stream1) ;
cudaStreamCreate (&stream2) ;
//cudaStreamCreate (&stream2) ;
return true;
}
@ -518,103 +339,57 @@ bool cuda_multicorrelator::set_local_code_and_taps(
int n_correlators
)
{
// local code CPU -> GPU copy memory
//********* 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=(float)code_length_chips;
// Correlator shifts vector CPU -> GPU copy memory (fractional chip shifts are allowed!)
//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::Carrier_wipeoff_multicorrelator_cuda(
bool cuda_multicorrelator::set_input_output_vectors(
std::complex<float>* corr_out,
const std::complex<float>* sig_in,
const std::complex<float>* local_codes_in,
float rem_carrier_phase_in_rad,
float phase_step_rad,
const int *shifts_samples,
int signal_length_samples,
int n_correlators)
std::complex<float>* sig_in
)
{
// 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;
size_t memSize = signal_length_samples * sizeof(std::complex<float>);
// input signal CPU -> GPU copy memory
cudaMemcpyAsync(d_sig_in, sig_in, memSize,
cudaMemcpyHostToDevice, stream1);
//***** NOTICE: NCO is computed on-the-fly, not need to copy NCO into GPU! ****
// (cudaMemcpyAsync(d_nco_in, nco_in, memSize,
// cudaMemcpyHostToDevice, stream1));
// old version: all local codes are independent vectors
// (cudaMemcpyAsync(d_local_codes_in, local_codes_in, memSize*n_correlators,
// cudaMemcpyHostToDevice, stream2));
// new version: only one vector with extra samples to shift the local code for the correlator set
// Required: The last correlator tap in d_shifts_samples has the largest sample shift
// local code CPU -> GPU copy memory
cudaMemcpyAsync(d_local_codes_in, local_codes_in, memSize+sizeof(std::complex<float>)*shifts_samples[n_correlators-1],
cudaMemcpyHostToDevice, stream2);
// Correlator shifts vector CPU -> GPU copy memory
cudaMemcpyAsync(d_shifts_samples, shifts_samples, sizeof(int)*n_correlators,
cudaMemcpyHostToDevice, stream2);
//Launch carrier wipe-off kernel here, while local codes are being copied to GPU!
cudaStreamSynchronize(stream1);
CUDA_32fc_Doppler_wipeoff<<<blocksPerGrid, threadsPerBlock,0, stream1>>>(d_sig_doppler_wiped, d_sig_in,rem_carrier_phase_in_rad,phase_step_rad, signal_length_samples);
//printf("CUDA kernel launch with %d blocks of %d threads\n", blocksPerGrid, threadsPerBlock);
//wait for Doppler wipeoff end...
cudaStreamSynchronize(stream1);
cudaStreamSynchronize(stream2);
// (cudaDeviceSynchronize());
//old
// scalarProdGPUCPXxN<<<blocksPerGrid, threadsPerBlock,0 ,stream2>>>(
// d_corr_out,
// d_sig_doppler_wiped,
// d_local_codes_in,
// 3,
// signal_length_samples
// );
//new
//launch the multitap correlator
scalarProdGPUCPXxN_shifts<<<blocksPerGrid, threadsPerBlock,0 ,stream2>>>(
d_corr_out,
d_sig_doppler_wiped,
d_local_codes_in,
d_shifts_samples,
n_correlators,
signal_length_samples
);
cudaGetLastError();
//wait for correlators end...
cudaStreamSynchronize(stream2);
// Copy the device result vector in device memory to the host result vector
// in host memory.
//scalar products (correlators outputs)
cudaMemcpy(corr_out, d_corr_out, sizeof(std::complex<float>)*n_correlators,
cudaMemcpyDeviceToHost);
return true;
}
bool cuda_multicorrelator::Carrier_wipeoff_multicorrelator_resampler_cuda(
std::complex<float>* corr_out,
const std::complex<float>* sig_in,
float rem_carrier_phase_in_rad,
float phase_step_rad,
float code_phase_step_chips,
@ -623,26 +398,40 @@ bool cuda_multicorrelator::Carrier_wipeoff_multicorrelator_resampler_cuda(
int n_correlators)
{
size_t memSize = signal_length_samples * sizeof(std::complex<float>);
// cudaMemCpy version
//size_t memSize = signal_length_samples * sizeof(std::complex<float>);
// input signal CPU -> GPU copy memory
cudaMemcpyAsync(d_sig_in, sig_in, memSize,
cudaMemcpyHostToDevice, stream2);
//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 carrier wipe-off kernel here, while local codes are being copied to GPU!
cudaStreamSynchronize(stream2);
//cudaStreamSynchronize(stream2);
CUDA_32fc_Doppler_wipeoff<<<blocksPerGrid, threadsPerBlock,0, stream2>>>(d_sig_doppler_wiped, d_sig_in,rem_carrier_phase_in_rad,phase_step_rad, signal_length_samples);
//CUDA_32fc_Doppler_wipeoff<<<blocksPerGrid, threadsPerBlock,0, stream1>>>(d_sig_doppler_wiped, d_sig_in,rem_carrier_phase_in_rad,phase_step_rad, signal_length_samples);
//wait for Doppler wipeoff end...
cudaStreamSynchronize(stream1);
cudaStreamSynchronize(stream2);
//cudaStreamSynchronize(stream1);
//cudaStreamSynchronize(stream2);
//launch the multitap correlator with integrated local code resampler!
scalarProdGPUCPXxN_shifts_chips<<<blocksPerGrid, threadsPerBlock,0 ,stream1>>>(
// scalarProdGPUCPXxN_shifts_chips<<<blocksPerGrid, threadsPerBlock,0 ,stream1>>>(
// d_corr_out,
// 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
// );
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,
@ -650,23 +439,33 @@ bool cuda_multicorrelator::Carrier_wipeoff_multicorrelator_resampler_cuda(
code_phase_step_chips,
rem_code_phase_chips,
n_correlators,
signal_length_samples
);
signal_length_samples,
rem_carrier_phase_in_rad,
phase_step_rad
);
cudaGetLastError();
//debug
// std::complex<float>* debug_signal;
// debug_signal=static_cast<std::complex<float>*>(malloc(memSize));
// cudaMemcpyAsync(debug_signal, d_sig_doppler_wiped, memSize,
// cudaMemcpyDeviceToHost,stream1);
// cudaStreamSynchronize(stream1);
// std::cout<<"d_sig_doppler_wiped GPU="<<debug_signal[456]<<","<<debug_signal[1]<<","<<debug_signal[2]<<","<<debug_signal[3]<<std::endl;
//cudaGetLastError();
//wait for correlators end...
cudaStreamSynchronize(stream1);
//cudaStreamSynchronize(stream1);
// Copy the device result vector in device memory to the host result vector
// in host memory.
//scalar products (correlators outputs)
cudaMemcpyAsync(corr_out, d_corr_out, sizeof(std::complex<float>)*n_correlators,
cudaMemcpyDeviceToHost,stream1);
//cudaMemcpyAsync(corr_out, d_corr_out, sizeof(std::complex<float>)*n_correlators,
// cudaMemcpyDeviceToHost,stream1);
cudaStreamSynchronize(stream1);
return true;
}
cuda_multicorrelator::cuda_multicorrelator()
{
d_sig_in=NULL;
@ -689,22 +488,16 @@ bool cuda_multicorrelator::free_cuda()
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);
cudaStreamDestroy(stream1) ;
cudaStreamDestroy(stream2) ;
// 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());
cudaDeviceReset();
return true;
}

24
src/algorithms/tracking/libs/cuda_multicorrelator.h
View File

@ -114,9 +114,7 @@ class cuda_multicorrelator
{
public:
cuda_multicorrelator();
bool init_cuda(const int argc, const char **argv, int signal_length_samples, int local_codes_length_samples, int n_correlators);
bool init_cuda_integrated_resampler(
const int argc, const char **argv,
int signal_length_samples,
int code_length_chips,
int n_correlators
@ -127,19 +125,12 @@ public:
float *shifts_chips,
int n_correlators
);
bool set_input_output_vectors(
std::complex<float>* corr_out,
std::complex<float>* sig_in
);
bool free_cuda();
bool Carrier_wipeoff_multicorrelator_cuda(
std::complex<float>* corr_out,
const std::complex<float>* sig_in,
const std::complex<float>* local_codes_in,
float rem_carrier_phase_in_rad,
float phase_step_rad,
const int *shifts_samples,
int signal_length_samples,
int n_correlators);
bool Carrier_wipeoff_multicorrelator_resampler_cuda(
std::complex<float>* corr_out,
const std::complex<float>* sig_in,
float rem_carrier_phase_in_rad,
float phase_step_rad,
float code_phase_step_chips,
@ -154,6 +145,11 @@ private:
GPU_Complex *d_sig_doppler_wiped;
GPU_Complex *d_local_codes_in;
GPU_Complex *d_corr_out;
//
std::complex<float> *d_sig_in_cpu;
std::complex<float> *d_corr_out_cpu;
int *d_shifts_samples;
float *d_shifts_chips;
float d_code_length_chips;
@ -162,7 +158,7 @@ private:
int blocksPerGrid;
cudaStream_t stream1;
cudaStream_t stream2;
//cudaStream_t stream2;
int num_gpu_devices;
int selected_device;
};