炼数成金CUDA视频教程——第三课1——学习笔记

/***
* gputimer.h 源程序来自炼数成金教程
* ***/
#ifndef __GPU_TIMER_H__
#define __GPU_TIMER_H__

struct GpuTimer
{
cudaEvent_t start;
cudaEvent_t stop;

GpuTimer()
{
cudaEventCreate(&start);
cudaEventCreate(&stop);
}

~GpuTimer()
{
cudaEventDestroy(start);
cudaEventDestroy(stop);
}

void Start()
{
cudaEventRecord(start, 0);
}

void Stop()
{
cudaEventRecord(stop, 0);
}

float Elapsed()
{
float elapsed;
cudaEventSynchronize(stop);
cudaEventElapsedTime(&elapsed, start, stop);
return elapsed;
}
};

#endif  /* __GPU_TIMER_H__ */

////////////////////////////////////////////////////////////////////////////////////

////////////////////////////////////////////////////////////////////////////////////

/****
* reduce.cu 源程序来自炼数成金教程
* ***/
#include <stdio.h>
#include <stdlib.h>
#include <cuda_runtime.h>

__global__ void global_reduce_kernel(float * d_out, float * d_in)
{
int myId = threadIdx.x + blockDim.x * blockIdx.x;
int tid  = threadIdx.x;

// do reduction in global mem
for (unsigned int s = blockDim.x / 2; s > 0; s >>= 1)
{
if (tid < s)
{
d_in[myId] += d_in[myId + s];
}
__syncthreads();        // make sure all adds at one stage are done!
}

// only thread 0 writes result for this block back to global mem
if (tid == 0)
{
d_out[blockIdx.x] = d_in[myId];
}
}

__global__ void shmem_reduce_kernel(float * d_out, const float * d_in)
{
// sdata is allocated in the kernel call: 3rd arg to <<<b, t, shmem>>>
extern __shared__ float sdata[];

int myId = threadIdx.x + blockDim.x * blockIdx.x;
int tid  = threadIdx.x;

// load shared mem from global mem
sdata[tid] = d_in[myId];
__syncthreads();            // make sure entire block is loaded!

// do reduction in shared mem
for (unsigned int s = blockDim.x / 2; s > 0; s >>= 1)
{
if (tid < s)
{
sdata[tid] += sdata[tid + s];
}
__syncthreads();        // make sure all adds at one stage are done!
}

// only thread 0 writes result for this block back to global mem
if (tid == 0)
{
d_out[blockIdx.x] = sdata[0];
}
}

void reduce(float * d_out, float * d_intermediate, float * d_in,
int size, bool usesSharedMemory)
{
// assumes that size is not greater than maxThreadsPerBlock^2
// and that size is a multiple of maxThreadsPerBlock
const int maxThreadsPerBlock = 1024;
int threads = maxThreadsPerBlock;
int blocks = size / maxThreadsPerBlock;
if (usesSharedMemory)
{
shmem_reduce_kernel<<<blocks, threads, threads * sizeof(float)>>>
(d_intermediate, d_in);
}
else
{
global_reduce_kernel<<<blocks, threads>>>
(d_intermediate, d_in);
}
// now we're down to one block left, so reduce it
threads = blocks; // launch one thread for each block in prev step
blocks = 1;
if (usesSharedMemory)
{
shmem_reduce_kernel<<<blocks, threads, threads * sizeof(float)>>>
(d_out, d_intermediate);
}
else
{
global_reduce_kernel<<<blocks, threads>>>
(d_out, d_intermediate);
}
}

int main(int argc, char **argv)
{
int deviceCount;
cudaGetDeviceCount(&deviceCount);
if (deviceCount == 0) {
fprintf(stderr, "error: no devices supporting CUDA.\n");
exit(EXIT_FAILURE);
}
int dev = 0;
cudaSetDevice(dev);

cudaDeviceProp devProps;
if (cudaGetDeviceProperties(&devProps, dev) == 0)
{
printf("Using device %d:\n", dev);
printf("%s; global mem: %dB; compute v%d.%d; clock: %d kHz\n",
devProps.name, (int)devProps.totalGlobalMem,
(int)devProps.major, (int)devProps.minor,
(int)devProps.clockRate);
}

const int ARRAY_SIZE = 1 << 20;
const int ARRAY_BYTES = ARRAY_SIZE * sizeof(float);

// generate the input array on the host
float h_in[ARRAY_SIZE];
float sum = 0.0f;
for(int i = 0; i < ARRAY_SIZE; i++) {
// generate random float in [-1.0f, 1.0f]
h_in[i] = -1.0f + (float)random()/((float)RAND_MAX/2.0f);
sum += h_in[i];
}

// declare GPU memory pointers
float * d_in, * d_intermediate, * d_out;

// allocate GPU memory
cudaMalloc((void **) &d_in, ARRAY_BYTES);
cudaMalloc((void **) &d_intermediate, ARRAY_BYTES); // overallocated
cudaMalloc((void **) &d_out, sizeof(float));

// transfer the input array to the GPU
cudaMemcpy(d_in, h_in, ARRAY_BYTES, cudaMemcpyHostToDevice);

int whichKernel = 0;
if (argc == 2) {
whichKernel = atoi(argv[1]);
}

cudaEvent_t start, stop;
cudaEventCreate(&start
9819
);
cudaEventCreate(&stop);
// launch the kernel
switch(whichKernel) {
case 0:
printf("Running global reduce\n");
cudaEventRecord(start, 0);
for (int i = 0; i < 100; i++)
{
reduce(d_out, d_intermediate, d_in, ARRAY_SIZE, false);
}
cudaEventRecord(stop, 0);
break;
case 1:
printf("Running reduce with shared mem\n");
cudaEventRecord(start, 0);
for (int i = 0; i < 100; i++)
{
reduce(d_out, d_intermediate, d_in, ARRAY_SIZE, true);
}
cudaEventRecord(stop, 0);
break;
default:
fprintf(stderr, "error: ran no kernel\n");
exit(EXIT_FAILURE);
}
cudaEventSynchronize(stop);
float elapsedTime;
cudaEventElapsedTime(&elapsedTime, start, stop);
elapsedTime /= 100.0f;      // 100 trials

// copy back the sum from GPU
float h_out;
cudaMemcpy(&h_out, d_out, sizeof(float), cudaMemcpyDeviceToHost);

printf("average time elapsed: %f\n", elapsedTime);

// free GPU memory allocation
cudaFree(d_in);
cudaFree(d_intermediate);
cudaFree(d_out);

return 0;
}