577 lines
19 KiB
Text
577 lines
19 KiB
Text
/*
|
|
# Copyright (c) 2011-2012 NVIDIA CORPORATION. All Rights Reserved.
|
|
#
|
|
# NVIDIA CORPORATION and its licensors retain all intellectual property
|
|
# and proprietary rights in and to this software, related documentation
|
|
# and any modifications thereto. Any use, reproduction, disclosure or
|
|
# distribution of this software and related documentation without an express
|
|
# license agreement from NVIDIA CORPORATION is strictly prohibited.
|
|
*/
|
|
|
|
#include <iostream>
|
|
#include <cuda_runtime_api.h>
|
|
#include <omp.h>
|
|
#include <thrust/reduce.h>
|
|
#include <thrust/device_ptr.h>
|
|
#include <thrust/execution_policy.h>
|
|
#include "GpuTimer.h"
|
|
|
|
#define CUDA_SAFE_CALL(call) \
|
|
{ \
|
|
cudaError_t err_code = call; \
|
|
if( err_code != cudaSuccess ) { std::cerr << "Error (" << __FILE__ << ":" << __LINE__ << "): " << cudaGetErrorString(err_code) << std::endl; return 1; } \
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
// G P U R E D U C T I O N
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
__global__ void reduce_kernel( int n, const int *in_buffer, int *out_buffer, const int2 *block_ranges )
|
|
{
|
|
// Allocate shared memory inside the block.
|
|
extern __shared__ int s_mem[];
|
|
|
|
// The range of data to work with.
|
|
int2 range = block_ranges[blockIdx.x];
|
|
|
|
// Compute the sum of my elements.
|
|
int my_sum = 0;
|
|
for( int idx = range.x + threadIdx.x ; idx < range.y ; idx += blockDim.x )
|
|
my_sum += in_buffer[idx];
|
|
|
|
// Copy my sum in shared memory.
|
|
s_mem[threadIdx.x] = my_sum;
|
|
|
|
// Make sure all the threads have copied their value in shared memory.
|
|
__syncthreads();
|
|
|
|
int offset ;
|
|
// Compute the sum inside the block.
|
|
for(offset = blockDim.x / 2 ; offset > 16 ; offset /= 2 )
|
|
{
|
|
if( threadIdx.x < offset )
|
|
s_mem[threadIdx.x] += s_mem[threadIdx.x + offset];
|
|
__syncthreads( );
|
|
}
|
|
|
|
//INSIDE WARP 0 SYNC NOT NECESSARY
|
|
for(; offset > 0 ; offset /= 2 )
|
|
{
|
|
if( threadIdx.x < offset )
|
|
s_mem[threadIdx.x] += s_mem[threadIdx.x + offset];
|
|
//__syncthreads( );
|
|
}
|
|
|
|
|
|
// The first thread of the block stores its result.
|
|
if( threadIdx.x == 0 )
|
|
out_buffer[blockIdx.x] = s_mem[0];
|
|
}
|
|
|
|
int reduce_on_gpu( int n, const int *a_device )
|
|
{
|
|
// Compute the size of the grid.
|
|
const int BLOCK_DIM = 256;
|
|
const int grid_dim = std::min( BLOCK_DIM, (n + BLOCK_DIM-1) / BLOCK_DIM );
|
|
const int num_threads = BLOCK_DIM * grid_dim;
|
|
|
|
// Compute the number of elements per block.
|
|
const int elements_per_block = BLOCK_DIM * ((n + num_threads - 1) / num_threads);
|
|
|
|
// Allocate memory for temporary buffers.
|
|
int *partial_sums = NULL;
|
|
int2 *block_ranges = NULL;
|
|
|
|
CUDA_SAFE_CALL( cudaMalloc( (void **) &partial_sums, BLOCK_DIM * sizeof(int ) ) );
|
|
CUDA_SAFE_CALL( cudaMalloc( (void **) &block_ranges, grid_dim * sizeof(int2) ) );
|
|
|
|
// Compute the ranges for the blocks.
|
|
int sum = 0;
|
|
int2 *block_ranges_on_host = new int2[grid_dim];
|
|
for( int block_idx = 0 ; block_idx < grid_dim ; ++block_idx )
|
|
{
|
|
block_ranges_on_host[block_idx].x = sum;
|
|
block_ranges_on_host[block_idx].y = std::min( sum += elements_per_block, n );
|
|
}
|
|
CUDA_SAFE_CALL( cudaMemcpy( block_ranges, block_ranges_on_host, grid_dim * sizeof(int2), cudaMemcpyHostToDevice ) );
|
|
delete[] block_ranges_on_host;
|
|
|
|
// First round: Compute a partial sum for all blocks.
|
|
reduce_kernel<<<grid_dim, BLOCK_DIM, BLOCK_DIM*sizeof(int)>>>( n, a_device, partial_sums, block_ranges );
|
|
CUDA_SAFE_CALL( cudaGetLastError() );
|
|
|
|
// Set the ranges for the second kernel call.
|
|
int2 block_range = make_int2( 0, grid_dim );
|
|
CUDA_SAFE_CALL( cudaMemcpy( block_ranges, &block_range, sizeof(int2), cudaMemcpyHostToDevice ) );
|
|
|
|
// Second round: Compute the final sum by summing the partial results of all blocks.
|
|
reduce_kernel<<<1, BLOCK_DIM, BLOCK_DIM*sizeof(int)>>>( grid_dim, partial_sums, partial_sums, block_ranges );
|
|
CUDA_SAFE_CALL( cudaGetLastError() );
|
|
|
|
// Read the result from device memory.
|
|
int result;
|
|
CUDA_SAFE_CALL( cudaMemcpy( &result, partial_sums, sizeof(int), cudaMemcpyDeviceToHost ) );
|
|
|
|
// Free temporary memory.
|
|
CUDA_SAFE_CALL( cudaFree( block_ranges ) );
|
|
CUDA_SAFE_CALL( cudaFree( partial_sums ) );
|
|
|
|
return result;
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
// G P U R E D U C T I O N : O P T I M I Z E D V E R S I O N
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
#define WARP_SIZE 32
|
|
|
|
template< int BLOCK_DIM >
|
|
__global__ void reduce_kernel_optimized( int n, const int *in_buffer, int *out_buffer, const int2 *__restrict block_ranges )
|
|
{
|
|
// The number of warps in the block.
|
|
const int NUM_WARPS = BLOCK_DIM / WARP_SIZE;
|
|
|
|
// Allocate shared memory inside the block.
|
|
__shared__ volatile int s_mem[BLOCK_DIM];
|
|
|
|
// The range of data to work with.
|
|
int2 range = block_ranges[blockIdx.x];
|
|
|
|
// Warp/lane IDs.
|
|
const int warp_id = threadIdx.x / WARP_SIZE;
|
|
const int lane_id = threadIdx.x % WARP_SIZE;
|
|
|
|
// Compute the sum of my elements.
|
|
int my_sum = 0;
|
|
for( int idx = range.x + threadIdx.x ; idx < range.y ; idx += BLOCK_DIM )
|
|
my_sum += in_buffer[idx];
|
|
|
|
// Copy my sum in shared memory.
|
|
s_mem[threadIdx.x] = my_sum;
|
|
|
|
// Compute the sum inside each warp.
|
|
#pragma unroll
|
|
for( int offset = 16 ; offset > 1 ; offset >>= 1 )
|
|
if( lane_id < offset )
|
|
s_mem[threadIdx.x] = my_sum += s_mem[threadIdx.x + offset];
|
|
|
|
__syncthreads();
|
|
|
|
// Each warp leader stores the result for the warp.
|
|
if( lane_id == 0 )
|
|
s_mem[warp_id] = my_sum += s_mem[threadIdx.x+1];
|
|
|
|
__syncthreads();
|
|
|
|
if( warp_id == 0 )
|
|
{
|
|
// Read my value from shared memory and store it in a register.
|
|
my_sum = s_mem[lane_id];
|
|
|
|
// Sum the results of the warps.
|
|
#pragma unroll
|
|
for( int offset = NUM_WARPS / 2 ; offset > 1 ; offset >>= 1 )
|
|
if( threadIdx.x < offset )
|
|
s_mem[threadIdx.x] = my_sum += s_mem[threadIdx.x + offset];
|
|
}
|
|
|
|
// The 1st thread stores the result of the block.
|
|
if( threadIdx.x == 0 )
|
|
out_buffer[blockIdx.x] = my_sum += s_mem[1];
|
|
}
|
|
|
|
template< int BLOCK_DIM >
|
|
int reduce_on_gpu_optimized( int n, const int *a_device )
|
|
{
|
|
// Compute the size of the grid.
|
|
const int grid_dim = std::min( BLOCK_DIM, (n + BLOCK_DIM-1) / BLOCK_DIM );
|
|
const int num_threads = BLOCK_DIM * grid_dim;
|
|
|
|
// Compute the number of elements per block.
|
|
const int elements_per_block = BLOCK_DIM * ((n + num_threads - 1) / num_threads);
|
|
|
|
// Allocate memory for temporary buffers.
|
|
int *partial_sums = NULL;
|
|
int2 *block_ranges = NULL;
|
|
|
|
CUDA_SAFE_CALL( cudaMalloc( (void **) &partial_sums, BLOCK_DIM * sizeof(int ) ) );
|
|
CUDA_SAFE_CALL( cudaMalloc( (void **) &block_ranges, grid_dim * sizeof(int2) ) );
|
|
|
|
// Compute the ranges for the blocks.
|
|
int sum = 0;
|
|
int2 *block_ranges_on_host = new int2[grid_dim];
|
|
for( int block_idx = 0 ; block_idx < grid_dim ; ++block_idx )
|
|
{
|
|
block_ranges_on_host[block_idx].x = sum;
|
|
block_ranges_on_host[block_idx].y = std::min( sum += elements_per_block, n );
|
|
}
|
|
CUDA_SAFE_CALL( cudaMemcpy( block_ranges, block_ranges_on_host, grid_dim * sizeof(int2), cudaMemcpyHostToDevice ) );
|
|
delete[] block_ranges_on_host;
|
|
|
|
// First round: Compute a partial sum for all blocks.
|
|
reduce_kernel_optimized<BLOCK_DIM><<<grid_dim, BLOCK_DIM>>>( n, a_device, partial_sums, block_ranges );
|
|
CUDA_SAFE_CALL( cudaGetLastError() );
|
|
|
|
// Set the ranges for the second kernel call.
|
|
int2 block_range = make_int2( 0, grid_dim );
|
|
CUDA_SAFE_CALL( cudaMemcpy( block_ranges, &block_range, sizeof(int2), cudaMemcpyHostToDevice ) );
|
|
|
|
// Second round: Compute the final sum by summing the partial results of all blocks.
|
|
reduce_kernel_optimized<BLOCK_DIM><<<1, BLOCK_DIM>>>( grid_dim, partial_sums, partial_sums, block_ranges );
|
|
CUDA_SAFE_CALL( cudaGetLastError() );
|
|
|
|
// Read the result from device memory.
|
|
int result;
|
|
CUDA_SAFE_CALL( cudaMemcpy( &result, partial_sums, sizeof(int), cudaMemcpyDeviceToHost ) );
|
|
|
|
// Free temporary memory.
|
|
CUDA_SAFE_CALL( cudaFree( block_ranges ) );
|
|
CUDA_SAFE_CALL( cudaFree( partial_sums ) );
|
|
|
|
return result;
|
|
}
|
|
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
// G P U R E D U C T I O N : O P T I M I Z E D WITHOUT MYSUM+= V E R S I O N
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
|
|
template< int BLOCK_DIM >
|
|
__global__ void reduce_kernel_optimized_wo_mysum( int n, const int *in_buffer, int *out_buffer, const int2 *__restrict block_ranges )
|
|
{
|
|
// The number of warps in the block.
|
|
const int NUM_WARPS = BLOCK_DIM / WARP_SIZE;
|
|
|
|
// Allocate shared memory inside the block.
|
|
__shared__ volatile int s_mem[BLOCK_DIM];
|
|
|
|
// The range of data to work with.
|
|
int2 range = block_ranges[blockIdx.x];
|
|
|
|
// Warp/lane IDs.
|
|
const int warp_id = threadIdx.x / WARP_SIZE;
|
|
const int lane_id = threadIdx.x % WARP_SIZE;
|
|
|
|
// Compute the sum of my elements.
|
|
int my_sum = 0;
|
|
for( int idx = range.x + threadIdx.x ; idx < range.y ; idx += BLOCK_DIM )
|
|
my_sum += in_buffer[idx];
|
|
|
|
// Copy my sum in shared memory.
|
|
s_mem[threadIdx.x] = my_sum;
|
|
|
|
// Compute the sum inside each warp.
|
|
#pragma unroll
|
|
for( int offset = 16 ; offset > 0 ; offset >>= 1 )
|
|
if( lane_id < offset )
|
|
s_mem[threadIdx.x] += s_mem[threadIdx.x + offset];
|
|
|
|
__syncthreads();
|
|
|
|
// Each warp leader stores the result for the warp.
|
|
if( lane_id == 0 )
|
|
s_mem[warp_id] = s_mem[threadIdx.x];
|
|
|
|
__syncthreads();
|
|
|
|
if( warp_id == 0 )
|
|
{
|
|
// Sum the results of the warps.
|
|
#pragma unroll
|
|
for( int offset = NUM_WARPS / 2 ; offset > 0 ; offset >>= 1 )
|
|
if( threadIdx.x < offset )
|
|
s_mem[threadIdx.x] += s_mem[threadIdx.x + offset];
|
|
}
|
|
|
|
// The 1st thread stores the result of the block.
|
|
if( threadIdx.x == 0 )
|
|
out_buffer[blockIdx.x] = s_mem[0];
|
|
}
|
|
|
|
template< int BLOCK_DIM >
|
|
int reduce_on_gpu_optimized_wo_mysum( int n, const int *a_device )
|
|
{
|
|
// Compute the size of the grid.
|
|
const int grid_dim = std::min( BLOCK_DIM, (n + BLOCK_DIM-1) / BLOCK_DIM );
|
|
const int num_threads = BLOCK_DIM * grid_dim;
|
|
|
|
// Compute the number of elements per block.
|
|
const int elements_per_block = BLOCK_DIM * ((n + num_threads - 1) / num_threads);
|
|
|
|
// Allocate memory for temporary buffers.
|
|
int *partial_sums = NULL;
|
|
int2 *block_ranges = NULL;
|
|
|
|
CUDA_SAFE_CALL( cudaMalloc( (void **) &partial_sums, BLOCK_DIM * sizeof(int ) ) );
|
|
CUDA_SAFE_CALL( cudaMalloc( (void **) &block_ranges, grid_dim * sizeof(int2) ) );
|
|
|
|
// Compute the ranges for the blocks.
|
|
int sum = 0;
|
|
int2 *block_ranges_on_host = new int2[grid_dim];
|
|
for( int block_idx = 0 ; block_idx < grid_dim ; ++block_idx )
|
|
{
|
|
block_ranges_on_host[block_idx].x = sum;
|
|
block_ranges_on_host[block_idx].y = std::min( sum += elements_per_block, n );
|
|
}
|
|
CUDA_SAFE_CALL( cudaMemcpy( block_ranges, block_ranges_on_host, grid_dim * sizeof(int2), cudaMemcpyHostToDevice ) );
|
|
delete[] block_ranges_on_host;
|
|
|
|
// First round: Compute a partial sum for all blocks.
|
|
reduce_kernel_optimized_wo_mysum<BLOCK_DIM><<<grid_dim, BLOCK_DIM>>>( n, a_device, partial_sums, block_ranges );
|
|
CUDA_SAFE_CALL( cudaGetLastError() );
|
|
|
|
// Set the ranges for the second kernel call.
|
|
int2 block_range = make_int2( 0, grid_dim );
|
|
CUDA_SAFE_CALL( cudaMemcpy( block_ranges, &block_range, sizeof(int2), cudaMemcpyHostToDevice ) );
|
|
|
|
// Second round: Compute the final sum by summing the partial results of all blocks.
|
|
reduce_kernel_optimized_wo_mysum<BLOCK_DIM><<<1, BLOCK_DIM>>>( grid_dim, partial_sums, partial_sums, block_ranges );
|
|
CUDA_SAFE_CALL( cudaGetLastError() );
|
|
|
|
// Read the result from device memory.
|
|
int result;
|
|
CUDA_SAFE_CALL( cudaMemcpy( &result, partial_sums, sizeof(int), cudaMemcpyDeviceToHost ) );
|
|
|
|
// Free temporary memory.
|
|
CUDA_SAFE_CALL( cudaFree( block_ranges ) );
|
|
CUDA_SAFE_CALL( cudaFree( partial_sums ) );
|
|
|
|
return result;
|
|
}
|
|
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
// M A I N
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
int main( int, char ** )
|
|
{
|
|
const int NUM_TESTS = 10;
|
|
|
|
// The number of elements in the problem.
|
|
const int N = 256*256*1024;
|
|
//const int N = 256*256*8;
|
|
|
|
std::cout << "Computing a reduction on " << N << " elements" << std::endl;
|
|
|
|
// X and Y on the host (CPU).
|
|
int *a_host = new int[N];
|
|
|
|
// Make sure the memory got allocated. TODO: free memory.
|
|
if( a_host == NULL )
|
|
{
|
|
std::cerr << "ERROR: Couldn't allocate a_host" << std::endl;
|
|
return 1;
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
// Generate data
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
std::cout << "Filling with 1s" << std::endl;
|
|
|
|
// Generate pseudo-random data.
|
|
for( int i = 0 ; i < N ; ++i )
|
|
a_host[i] = 1;
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
// Compute on the CPU using 1 thread
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
std::cout << std::endl;
|
|
std::cout << "Computing on the CPU using 1 CPU thread" << std::endl;
|
|
|
|
GpuTimer gpu_timer;
|
|
gpu_timer.Start();
|
|
|
|
// Calculate the reference to compare with the device result.
|
|
int sum = 0;
|
|
for( int i_test = 0 ; i_test < NUM_TESTS ; ++i_test )
|
|
{
|
|
sum = 0;
|
|
for( int i = 0 ; i < N ; ++i )
|
|
sum += a_host[i];
|
|
}
|
|
|
|
gpu_timer.Stop();
|
|
|
|
std::cout << " Elapsed time: " << gpu_timer.Elapsed() / NUM_TESTS << "ms" << std::endl;
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
// Compute on the CPU using several OpenMP threads
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
std::cout << std::endl;
|
|
std::cout << "Computing on the CPU using " << omp_get_max_threads() << " OpenMP thread(s)" << std::endl;
|
|
|
|
gpu_timer.Start();
|
|
|
|
// Calculate the reference to compare with the device result.
|
|
int omp_sum = 0;
|
|
for( int i_test = 0 ; i_test < NUM_TESTS ; ++i_test )
|
|
{
|
|
omp_sum = 0;
|
|
#pragma omp parallel shared(omp_sum)
|
|
{
|
|
#pragma omp for reduction(+ : omp_sum)
|
|
for( int i = 0 ; i < N ; ++i )
|
|
omp_sum = omp_sum + a_host[i];
|
|
}
|
|
}
|
|
|
|
gpu_timer.Stop();
|
|
|
|
std::cout << " Elapsed time: " << gpu_timer.Elapsed() / NUM_TESTS << "ms" << std::endl;
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
// Compute on the GPU
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
// The copy of A on the device (GPU).
|
|
int *a_device = NULL;
|
|
|
|
// Allocate A on the device.
|
|
CUDA_SAFE_CALL( cudaMalloc( (void **) &a_device, N*sizeof( int ) ) );
|
|
|
|
// Copy A from host (CPU) to device (GPU).
|
|
CUDA_SAFE_CALL( cudaMemcpy( a_device, a_host, N*sizeof( int ), cudaMemcpyHostToDevice ) );
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
// Compute on the GPU using Thrust
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
std::cout << std::endl;
|
|
std::cout << "Computing on the GPU using Thrust (transfers excluded)" << std::endl;
|
|
|
|
gpu_timer.Start();
|
|
|
|
// Launch the kernel on the GPU.
|
|
int thrust_sum = 0;
|
|
thrust::device_ptr<int> aptr = thrust::device_pointer_cast(a_device);
|
|
for( int i_test = 0 ; i_test < NUM_TESTS ; ++i_test )
|
|
{
|
|
thrust_sum = thrust::reduce( aptr, aptr+N );
|
|
}
|
|
|
|
gpu_timer.Stop();
|
|
|
|
std::cout << " Elapsed time: " << gpu_timer.Elapsed() / NUM_TESTS << "ms" << std::endl;
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
// Compute on the GPU
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
std::cout << std::endl;
|
|
std::cout << "Computing on the GPU (transfers excluded)" << std::endl;
|
|
|
|
gpu_timer.Start();
|
|
|
|
// Launch the kernel on the GPU.
|
|
int gpu_sum = 0;
|
|
for( int i_test = 0 ; i_test < NUM_TESTS ; ++i_test )
|
|
{
|
|
gpu_sum = reduce_on_gpu( N, a_device );
|
|
}
|
|
|
|
gpu_timer.Stop();
|
|
|
|
std::cout << " Elapsed time: " << gpu_timer.Elapsed() / NUM_TESTS << "ms" << std::endl;
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
// Compute on the GPU (optimized version)
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
std::cout << std::endl;
|
|
std::cout << "Computing on the GPU using a tuned version (transfers excluded)" << std::endl;
|
|
|
|
gpu_timer.Start();
|
|
|
|
const int BLOCK_DIM = 256;
|
|
|
|
// Launch the kernel on the GPU.
|
|
int optim_gpu_sum = 0;
|
|
for( int i_test = 0 ; i_test < NUM_TESTS ; ++i_test )
|
|
{
|
|
optim_gpu_sum = reduce_on_gpu_optimized<BLOCK_DIM>( N, a_device );
|
|
}
|
|
|
|
gpu_timer.Stop();
|
|
|
|
std::cout << " Elapsed time: " << gpu_timer.Elapsed() / NUM_TESTS << "ms" << std::endl;
|
|
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
// Compute on the GPU (optimized version without mysum+=)
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
std::cout << std::endl;
|
|
std::cout << "Computing on the GPU using a tuned version without my_sum (transfers excluded)" << std::endl;
|
|
|
|
gpu_timer.Start();
|
|
|
|
|
|
// Launch the kernel on the GPU.
|
|
int optim_gpu_sum_wo_mysum = 0;
|
|
for( int i_test = 0 ; i_test < NUM_TESTS ; ++i_test )
|
|
{
|
|
optim_gpu_sum_wo_mysum = reduce_on_gpu_optimized_wo_mysum<BLOCK_DIM>( N, a_device );
|
|
}
|
|
|
|
gpu_timer.Stop();
|
|
|
|
std::cout << " Elapsed time: " << gpu_timer.Elapsed() / NUM_TESTS << "ms" << std::endl;
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
// Validate results
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
std::cout << std::endl;
|
|
std::cout << std::endl;
|
|
std::cout << "OpenMP results: ref= " << sum << " / sum= " << omp_sum << std::endl;
|
|
std::cout << "CUDA results: ref= " << sum << " / sum= " << gpu_sum << std::endl;
|
|
std::cout << "Thrust results: ref= " << sum << " / sum= " << thrust_sum << std::endl;
|
|
std::cout << "Optim results: ref= " << sum << " / sum= " << optim_gpu_sum << std::endl;
|
|
std::cout << "Optim without mysum+= results: ref= " << sum << " / sum= " << optim_gpu_sum_wo_mysum << std::endl;
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
// Clean memory
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
// Free device memory.
|
|
CUDA_SAFE_CALL( cudaFree( a_device ) );
|
|
|
|
// Free host memory.
|
|
delete[] a_host;
|
|
|
|
return 0;
|
|
}
|
|
|