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grid_gradient_GPU.cu
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Tue, Aug 20, 03:14

grid_gradient_GPU.cu
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#include <fstream>
#include "grid_gradient_GPU.cuh"
#include "gradient_GPU.cuh"
#define BLOCK_SIZE_X 32
#define BLOCK_SIZE_Y 16
//#define ROT
#define _SHARED_MEM
#ifdef _SHARED_MEM
#define SHARED __shared__
#warning "shared memory"
extern __shared__ type_t shared[];
#else
#define SHARED
#endif
#define Nx 1
#define Ny 0
#define cudasafe
extern "C"
{
double myseconds();
}
__global__ void module_potentialDerivatives_totalGradient_SOA_GPU(type_t *grid_grad_x, type_t *grid_grad_y, const struct Potential_SOA *lens, const struct grid_param *frame, int nbgridcells, int nhalos);
void gradient_grid_GPU_sorted(type_t *grid_grad_x, type_t *grid_grad_y, const struct grid_param *frame, const struct Potential_SOA *lens, int nhalos ,int nbgridcells)
{
int nBlocks_gpu = 0;
// Define the number of threads per block the GPU will use
cudaDeviceProp properties_gpu;
cudaGetDeviceProperties(&properties_gpu, 0); // Get properties of 0th GPU in use
if (properties_gpu.maxThreadsDim[0]<threadsPerBlock)
{
fprintf(stderr, "ERROR: The GPU has to support at least %u threads per block.\n", threadsPerBlock);
exit(-1);
}
else
{
nBlocks_gpu = properties_gpu.maxGridSize[0] / threadsPerBlock; // Get the maximum number of blocks with the chosen number of threads
// per Block that the GPU supports
}
grid_param *frame_gpu;
Potential_SOA *lens_gpu,*lens_kernel;
int *type_gpu;
type_t *lens_x_gpu, *lens_y_gpu, *b0_gpu, *angle_gpu, *epot_gpu, *rcore_gpu, *rcut_gpu, *anglecos_gpu, *anglesin_gpu;
type_t *grid_grad_x_gpu, *grid_grad_y_gpu ;
lens_gpu = (Potential_SOA *) malloc(sizeof(Potential_SOA));
lens_gpu->type = (int *) malloc(sizeof(int));
// Allocate variables on the GPU
cudasafe(cudaMalloc( (void**)&(lens_kernel), sizeof(Potential_SOA)),"Gradientgpu.cu : Alloc Potential_SOA: " );
cudasafe(cudaMalloc( (void**)&(type_gpu), nhalos*sizeof(int)),"Gradientgpu.cu : Alloc type_gpu: " );
cudasafe(cudaMalloc( (void**)&(lens_x_gpu), nhalos*sizeof(type_t)),"Gradientgpu.cu : Alloc x_gpu: " );
cudasafe(cudaMalloc( (void**)&(lens_y_gpu), nhalos*sizeof(type_t)),"Gradientgpu.cu : Alloc y_gpu: " );
cudasafe(cudaMalloc( (void**)&(b0_gpu), nhalos*sizeof(type_t)),"Gradientgpu.cu : Alloc b0_gpu: " );
cudasafe(cudaMalloc( (void**)&(angle_gpu), nhalos*sizeof(type_t)),"Gradientgpu.cu : Alloc angle_gpu: " );
cudasafe(cudaMalloc( (void**)&(epot_gpu), nhalos*sizeof(type_t)),"Gradientgpu.cu : Alloc epot_gpu: " );
cudasafe(cudaMalloc( (void**)&(rcore_gpu), nhalos*sizeof(type_t)),"Gradientgpu.cu : Alloc rcore_gpu: " );
cudasafe(cudaMalloc( (void**)&(rcut_gpu), nhalos*sizeof(type_t)),"Gradientgpu.cu : Alloc rcut_gpu: " );
cudasafe(cudaMalloc( (void**)&(anglecos_gpu), nhalos*sizeof(type_t)),"Gradientgpu.cu : Alloc anglecos_gpu: " );
cudasafe(cudaMalloc( (void**)&(anglesin_gpu), nhalos*sizeof(type_t)),"Gradientgpu.cu : Alloc anglesin_gpu: " );
cudasafe(cudaMalloc( (void**)&(frame_gpu), sizeof(grid_param)),"Gradientgpu.cu : Alloc frame_gpu: " );
cudasafe(cudaMalloc( (void**)&(grid_grad_x_gpu), (nbgridcells) * (nbgridcells) *sizeof(type_t)),"Gradientgpu.cu : Alloc source_x_gpu: " );
cudasafe(cudaMalloc( (void**)&(grid_grad_y_gpu), (nbgridcells) * (nbgridcells) *sizeof(type_t)),"Gradientgpu.cu : Alloc source_y_gpu: " );
// Copy values to the GPU
cudasafe(cudaMemcpy(type_gpu,lens->type , nhalos*sizeof(int),cudaMemcpyHostToDevice ),"Gradientgpu.cu : Copy type_gpu: " );
cudasafe(cudaMemcpy(lens_x_gpu,lens->position_x , nhalos*sizeof(type_t),cudaMemcpyHostToDevice ),"Gradientgpu.cu : Copy x_gpu: " );
cudasafe(cudaMemcpy(lens_y_gpu,lens->position_y , nhalos*sizeof(type_t), cudaMemcpyHostToDevice),"Gradientgpu.cu : Copy y_gpu: " );
cudasafe(cudaMemcpy(b0_gpu,lens->b0 , nhalos*sizeof(type_t), cudaMemcpyHostToDevice),"Gradientgpu.cu : Copy b0_gpu: " );
cudasafe(cudaMemcpy(angle_gpu,lens->ellipticity_angle , nhalos*sizeof(type_t), cudaMemcpyHostToDevice),"Gradientgpu.cu : Copy angle_gpu: " );
cudasafe(cudaMemcpy(epot_gpu, lens->ellipticity_potential, nhalos*sizeof(type_t),cudaMemcpyHostToDevice ),"Gradientgpu.cu : Copy epot_gpu: " );
cudasafe(cudaMemcpy(rcore_gpu, lens->rcore, nhalos*sizeof(type_t),cudaMemcpyHostToDevice ),"Gradientgpu.cu : Copy rcore_gpu: " );
cudasafe(cudaMemcpy(rcut_gpu, lens->rcut, nhalos*sizeof(type_t), cudaMemcpyHostToDevice),"Gradientgpu.cu : Copy rcut_gpu: " );
cudasafe(cudaMemcpy(anglecos_gpu, lens->anglecos, nhalos*sizeof(type_t),cudaMemcpyHostToDevice ),"Gradientgpu.cu : Copy anglecos: " );
cudasafe(cudaMemcpy(anglesin_gpu, lens->anglesin, nhalos*sizeof(type_t), cudaMemcpyHostToDevice),"Gradientgpu.cu : Copy anglesin: " );
cudasafe(cudaMemcpy(frame_gpu, frame, sizeof(grid_param), cudaMemcpyHostToDevice),"Gradientgpu.cu : Copy fame_gpu: " );
//
lens_gpu->type = type_gpu;
lens_gpu->position_x = lens_x_gpu;
lens_gpu->position_y = lens_y_gpu;
lens_gpu->b0 = b0_gpu;
lens_gpu->ellipticity_angle = angle_gpu;
lens_gpu->ellipticity_potential = epot_gpu;
lens_gpu->rcore = rcore_gpu;
lens_gpu->rcut = rcut_gpu;
lens_gpu->anglecos = anglecos_gpu;
lens_gpu->anglesin = anglesin_gpu;
//
cudaMemcpy(lens_kernel, lens_gpu, sizeof(Potential_SOA), cudaMemcpyHostToDevice);
//
double time = -myseconds();
module_potentialDerivatives_totalGradient_SOA_CPU_GPU(grid_grad_x_gpu, grid_grad_y_gpu, frame_gpu, lens, lens_kernel, nbgridcells, nhalos);
//
//cudasafe(cudaGetLastError(), "module_potentialDerivative_totalGradient_SOA_CPU_GPU");
cudaDeviceSynchronize();
time += myseconds();
//std::cout << " kernel time = " << time << " s." << std::endl;
//
cudasafe(cudaMemcpy( grid_grad_x, grid_grad_x_gpu, (nbgridcells)*(nbgridcells)*sizeof(type_t), cudaMemcpyDeviceToHost )," --- Gradientgpu.cu : Copy source_x_gpu: " );
cudasafe(cudaMemcpy( grid_grad_y, grid_grad_y_gpu, (nbgridcells)*(nbgridcells)*sizeof(type_t), cudaMemcpyDeviceToHost)," --- Gradientgpu.cu : Copy source_y_gpu: " );
//
//printf("-----> %f %f \n",grid_grad_x[Nx], grid_grad_y[Ny]);
// Free GPU memory
cudaFree(lens_gpu);
cudaFree(type_gpu);
cudaFree(lens_x_gpu);
cudaFree(lens_y_gpu);
cudaFree(b0_gpu);
cudaFree(angle_gpu);
cudaFree(epot_gpu);
cudaFree(rcore_gpu);
cudaFree(rcut_gpu);
cudaFree(anglecos_gpu);
cudaFree(anglesin_gpu);
cudaFree(grid_grad_x_gpu);
cudaFree(grid_grad_y_gpu);
}
__global__
void
module_potentialDerivatives_totalGradient_8_SOA_GPU_loc(type_t *grid_grad_x, type_t *grid_grad_y, const struct Potential_SOA
*lens, const struct grid_param *frame, int nbgridcells, int shalos, int nhalos)
{
//
//asm volatile("# module_potentialDerivatives_totalGradient_SOA begins");
// 6 DP loads, i.e. 48 Bytes: position_x, position_y, ellipticity_angle, ellipticity_potential, rcore, b0
//
type_t grad_x, grad_y;
type_t clumpgrad_x, clumpgrad_y;
type_t image_point_x, image_point_y;
//
SHARED type_t cosi [200];
SHARED type_t sinu [200];
SHARED type_t rci [200];
SHARED type_t b0 [200];
SHARED type_t epsi [200];
SHARED type_t position_x[200];
SHARED type_t position_y[200];
SHARED type_t rsqe [200];
//SHARED type_t sgrad_x [(BLOCK_SIZE_X + 1)*BLOCK_SIZE_Y];
//SHARED type_t sgrad_y [(BLOCK_SIZE_X + 1)*BLOCK_SIZE_Y];
//SHARED type_t sonepeps [200];
//SHARED type_t sonemeps [200];
//
grad_x = 0;
grad_y = 0;
//
int row = blockIdx.y*blockDim.y + threadIdx.y;
int col = blockIdx.x*blockDim.x + threadIdx.x;
//
//int loc_row = threadIdx.x;
//int loc_col = threadIdx.y*blockDim.x + threadIdx.x;
//
//int grid_size = nbgridcells/blockDim.y;
//
//if (threadIdx.x == 0) printf("%d %d %d: row = %d, col = %d, grid_size = %d\n", blockIdx.y, gridDim.y, threadIdx.y, row, col, grid_size);
//
type_t dx = (frame->xmax - frame->xmin)/(nbgridcells-1);
type_t dy = (frame->ymax - frame->ymin)/(nbgridcells-1);
//if (threadIdx.x == 0) printf("dx = %f, dy = %f\n", dx, dy);
//
image_point_x = frame->xmin + col*dx;
image_point_y = frame->ymin + row*dy;
//
//int iloc = threadIdx.x*blockDim.y + threadIdx.y;
int iglob = row*nbgridcells + col;
int numThreads = blockDim.x*blockDim.y;
//
for (int i = 0; i < (nhalos + numThreads - 1)/numThreads; ++i)
{
int iloc = threadIdx.y*blockDim.x + threadIdx.x + i*numThreads;
if (iloc < nhalos)
{
cosi[iloc] = __ldg(&lens->anglecos [shalos + iloc]);
sinu[iloc] = __ldg(&lens->anglesin [shalos + iloc]);
position_x[iloc] = __ldg(&lens->position_x [shalos + iloc]);
position_y[iloc] = __ldg(&lens->position_y [shalos + iloc]);
rci[iloc] = __ldg(&lens->rcore [shalos + iloc]);
b0[iloc] = __ldg(&lens->b0 [shalos + iloc]);
epsi[iloc] = __ldg(&lens->ellipticity_potential[shalos + iloc]);
rsqe[iloc] = sqrt(epsi[iloc]);
}
}
__syncthreads();
//
if ((row < nbgridcells) && (col < nbgridcells))
{
//
for(int i = 0; i < nhalos; i++)
{
//int index = iloc;
#if 1
type_t rc = rci[i];
type_t eps = epsi[i];
type_t onemeps = 1 - eps;
type_t onepeps = 1 + eps;
//
type_t sqe = rsqe[i];
type_t cx1 = onemeps/onepeps;
//
//
//type_t zci_im = 1;
type_t zci_im = -0.5*(1. - eps*eps)/sqe;
type_t inv_onepeps = 1./(onepeps*onepeps);
type_t inv_onemeps = 1./(onemeps*onemeps);
#endif
//
{
//KERNEL_8;
type_t grad_x = grad_y = 0.;
type_t true_coord_y = image_point_y - position_y[i];
type_t true_coord_x = image_point_x - position_x[i];
KERNEL_8_reg(0);
grid_grad_x[iglob + 0] += grad_x;
grid_grad_y[iglob + 0] += grad_y;
}
/*
{
//KERNEL_8;
type_t grad_x = grad_y = 0.;
type_t true_coord_y = image_point_y - position_y[i];
type_t true_coord_x = image_point_x - position_x[i] + BLOCK_SIZE_X/2*dx;
KERNEL_8_reg(0);
grid_grad_x[iglob + BLOCK_SIZE_X/2] += grad_x;
grid_grad_y[iglob + BLOCK_SIZE_X/2] += grad_y;
}
*/
//
}
}
}
void
module_potentialDerivatives_totalGradient_SOA_CPU_GPU(type_t *grid_grad_x, type_t *grid_grad_y, const struct grid_param *frame, const struct Potential_SOA *lens_cpu, const struct Potential_SOA *lens_gpu, int nbgridcells, int nhalos)
{
struct point grad, clumpgrad;
//
grad.x = clumpgrad.x = 0.;
grad.y = clumpgrad.y = 0.;
//
int shalos = 0;
//
int GRID_SIZE_X = (nbgridcells + BLOCK_SIZE_X - 1)/BLOCK_SIZE_X; // number of blocks
int GRID_SIZE_Y = (nbgridcells + BLOCK_SIZE_Y - 1)/BLOCK_SIZE_Y;
//printf("grid_size_x = %d, grid_size_y = %d\n", GRID_SIZE_X, GRID_SIZE_Y);
//
type_t* timer = (type_t *) malloc((int) nbgridcells*nbgridcells*sizeof(type_t));
double* dtimer;
//cudasafe(cudaMalloc( (void**)&(dtimer), (int) nbgridcells*nbgridcells*sizeof(double)),"Gradientgpu.cu : totalGradient_SOA_CPU_GPU: " );
//
{
dim3 threads(BLOCK_SIZE_X, BLOCK_SIZE_Y/1);
dim3 grid (GRID_SIZE_X , GRID_SIZE_Y);
//
int count = nhalos;
//printf("nhalos = %d, size of shared memory = %lf\n", nhalos, (type_t) (8*nhalos + BLOCK_SIZE_X*BLOCK_SIZE_Y)*sizeof(type_t));
//
cudaMemset(grid_grad_x, 0, nbgridcells*nbgridcells*sizeof(type_t));
cudaMemset(grid_grad_y, 0, nbgridcells*nbgridcells*sizeof(type_t));
//
//module_potentialDerivatives_totalGradient_8_SOA_GPU_cur<<<grid, threads>>> (grid_grad_x, grid_grad_y, lens_gpu, frame, nbgridcells, shalos, count);
module_potentialDerivatives_totalGradient_SOA_GPU<<<grid, threads>>> (grid_grad_x, grid_grad_y, lens_gpu, frame, nbgridcells, nhalos);
//module_potentialDerivatives_totalGradient_8_SOA_GPU_SM2<<<grid, threads>>> (grid_grad_x, grid_grad_y, lens_gpu, frame, nbgridcells, shalos, count);
//module_potentialDerivatives_totalGradient_8_SOA_GPU_SM4<<<dimGrid, dimBlock, (8*nhalos + BLOCK_SIZE_X*nbgridcells/BLOCK_SIZE_Y)*sizeof(type_t)>>> (grid_grad_x, grid_grad_y, lens_gpu, frame, nbgridcells, shalos, count/*, dtimer*/);
cudasafe(cudaGetLastError(), "module_potentialDerivative_totalGradient_SOA_CPU_GPU_8_SOA_GPU_SM4");
}
cudaDeviceSynchronize();
//printf("GPU kernel done... ");fflush(stdout);
//cudasafe(cudaMemcpy( timer, dtimer, nbgridcells*nbgridcells*sizeof(type_t), cudaMemcpyDeviceToHost ),"Gradientgpu.cu : dtimer memcpy " );
//
/*
{
dim3 dimBlock(BLOCK_SIZE/1, BLOCK_SIZE/2);
dim3 dimGrid(GRID_SIZE, GRID_SIZE);
//
int count = nhalos;
cudaMemset(grid_grad_x, 0, nbgridcells*nbgridcells*sizeof(type_t));
cudaMemset(grid_grad_y, 0, nbgridcells*nbgridcells*sizeof(type_t));
//
module_potentialDerivatives_totalGradient_8_SOA_GPU_SM<<<dimGrid, dimBlock>>> (grid_grad_x, grid_grad_y, lens_gpu, frame, nbgridcells, shalos, count);
}
*/
//
cudasafe(cudaGetLastError(), "module_potentialDerivative_totalGradient_SOA_CPU_GPU");
//
//printf("\n");
//module_potentialDerivatives_totalGradient_8_SOA_GPU_SM<<<dimGrid, dimBlock>>> (grid_grad_x, grid_grad_y, lens_gpu, frame, nbgridcells, shalos, count);
//grad.x += clumpgrad.x;
//grad.y += clumpgrad.y;
//
//
/*
while (shalos < nhalos)
{
int lens_type = lens_cpu->type[shalos];
int count = 1;
while (lens_cpu->type[shalos + count] == lens_type) count++;
//std::cerr << "type = " << lens_type << " " << count << " " << shalos << std::endl;
//printf ("%d %d %d \n",lens_type,count,shalos);
//
clumpgrad = (*halo_func_GPU[lens_type]<<<dimGrid, dimBlock>>> )(lens_gpu, shalos, count);
//
grad.x += clumpgrad.x;
grad.y += clumpgrad.y;
shalos += count;
}
return(grad);
*/
}

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