我正在尝试使用共享内存来提高测试代码的速度。代码include while循环从数组A和数组B执行一些计算，然后退出并将结果写入数组C。
问题在于，如果程序使用threads_per_block = 1，则共享内存快25％；如果程序使用threads_per_block = 1024，则共享内存快0％。
我期望共享内存可以提高速度。如果有问题，哪里出了问题？
我的系统规格：
i7 8750h 16GB双通道DDR4 2666 MHz内存 GTX1060移动6GB
#include <stdio.h>
#include <stdint.h>
#include <time.h>
#include <math.h>
#define threads_per_block 1024
#define blocks_per_grid 1024
#define shared_memory_size threads_per_block * 4 * 3
float clock_t1 = 0, clock_t2 = 0, clock_dt = 0;
void VectorAdd_cpu(float *A, float *B, float *C, int64_t n, int64_t repeat) {
uint32_t index = 0, j = 0;
for (index = 0; index < n; index++) {
j = 0;
while (j < repeat) {
j++;
C[index] = A[index] + B[index];
}
}
}
__global__ void VectorAdd_gpu(float *Ac, float *Bc, float *Cc, uint32_t n, uint32_t repeat) {
uint32_t index = blockIdx.x * blockDim.x + threadIdx.x;
uint32_t j;
if (index < n) {
j = 0;
while (j < repeat) {
j++;
Cc[index] = Ac[index] + Bc[index];
}
}
}
__global__ void VectorAdd_gpu_shared(float *Acs, float *Bcs, float *Ccs, uint32_t n, uint32_t repeat) {
uint32_t index = blockIdx.x * blockDim.x + threadIdx.x;
uint32_t tx = threadIdx.x;
uint32_t j;
float *Acs_temp, *Bcs_temp, *Ccs_temp;
extern __shared__ float S[];
Acs_temp = (float*)S;
Bcs_temp = (float*)Acs_temp + threads_per_block;
Ccs_temp = (float*)Bcs_temp + threads_per_block;
__syncthreads();
if (index < n) {
Acs_temp[tx] = Acs[index];
Bcs_temp[tx] = Bcs[index];
Ccs_temp[tx] = Ccs[index];
j = 0;
while (j < repeat) {
j++;
Ccs_temp[tx] = Acs_temp[tx] + Bcs_temp[tx];
}
Acs[index] = Acs_temp[tx];
Bcs[index] = Bcs_temp[tx];
Ccs[index] = Ccs_temp[tx];
}
}
int main() {
int nDevices;
cudaError_t err = cudaGetDeviceCount(&nDevices);
int64_t lenArray = threads_per_block * blocks_per_grid;
int64_t repeat = 0;
float *A, *B, *C, *Ac, *Bc, *Cc, *Acs, *Bcs, *Ccs;
uint64_t Size_array = lenArray * sizeof(float);
clock_t1 = float(clock());
A = (float *)malloc(Size_array);
B = (float *)malloc(Size_array);
C = (float *)malloc(Size_array);
clock_t2 = float(clock());printf("Cpu memory allocation : %.f ms\n", clock_t2 - clock_t1);
clock_t1 = float(clock());
cudaMallocManaged(&Ac, Size_array);
cudaMallocManaged(&Bc, Size_array);
cudaMallocManaged(&Cc, Size_array);
cudaMallocManaged(&Acs, Size_array);
cudaMallocManaged(&Bcs, Size_array);
cudaMallocManaged(&Ccs, Size_array);
clock_t2 = float(clock());printf("Gpu memory allocation : %.f ms\n", clock_t2 - clock_t1);
clock_t1 = float(clock());
for (int64_t i = 0; i < lenArray; i++) {
A[i] = i;
B[i] = i;
C[i] = 0;
}
clock_t2 = float(clock());printf("Cpu setting variable : %.f ms\n", clock_t2 - clock_t1);
clock_t1 = float(clock());printf("Cpu to Gpu copy variable :");
cudaMemcpy(Ac, A, Size_array, cudaMemcpyHostToDevice);
cudaMemcpy(Bc, B, Size_array, cudaMemcpyHostToDevice);
cudaMemcpy(Acs, A, Size_array, cudaMemcpyHostToDevice);
cudaMemcpy(Bcs, B, Size_array, cudaMemcpyHostToDevice);
clock_t2 = float(clock());printf("%.f ms\n", clock_t2 - clock_t1);
float temp1 = 0, temp2 = 0, temp3 = 0;
for (int i = 0;i <= 100;i++) {
repeat = pow(2, i);printf("Repeat : 2^%d\t", i);
clock_t1 = float(clock());printf("Cpu execution : ");
VectorAdd_cpu(A, B, C, lenArray, repeat);
clock_t2 = float(clock());printf("%.f\tms ", clock_t2 - clock_t1);
temp1 = clock_t2 - clock_t1;
clock_t1 = float(clock());printf("Gpu execution : ");
VectorAdd_gpu << <blocks_per_grid, threads_per_block >> > (Ac, Bc, Cc, lenArray, repeat);cudaDeviceSynchronize();
clock_t2 = float(clock());printf("%.f\tms ", clock_t2 - clock_t1);
temp2 = clock_t2 - clock_t1;
clock_t1 = float(clock());printf("Gpu_shared execution : ");
VectorAdd_gpu_shared << <blocks_per_grid, threads_per_block, shared_memory_size >> > (Acs, Bcs, Ccs, lenArray, repeat);cudaDeviceSynchronize();
clock_t2 = float(clock());printf("%.f\tms ", clock_t2 - clock_t1);
temp3 = clock_t2 - clock_t1;
printf("Gpu/Cpu : %.2f\t", temp1 / temp2);
printf("Gpu_shared/Gpu : %.2f\n", temp2 / temp3);
}
free(A);free(B);free(C);
cudaFree(Ac);cudaFree(Bc);cudaFree(Cc);
cudaFree(Acs);cudaFree(Bcs);cudaFree(Ccs);
printf("\n\nDone...\n\n");
return 1;
}
代码结果
Cpu memory allocation : 5 ms
Gpu memory allocation : 150 ms
Cpu setting variable : 2 ms
Cpu to Gpu copy variable :4 ms
Repeat : 2^0 Cpu execution : 9 ms Gpu execution : 5 ms Gpu_shared execution : 9 ms Gpu/Cpu : 1.80 Gpu_shared/Gpu : 0.56
Repeat : 2^1 Cpu execution : 21 ms Gpu execution : 8 ms Gpu_shared execution : 9 ms Gpu/Cpu : 2.63 Gpu_shared/Gpu : 0.89
Repeat : 2^2 Cpu execution : 39 ms Gpu execution : 8 ms Gpu_shared execution : 11 ms Gpu/Cpu : 4.88 Gpu_shared/Gpu : 0.73
Repeat : 2^3 Cpu execution : 77 ms Gpu execution : 3 ms Gpu_shared execution : 4 ms Gpu/Cpu : 25.67 Gpu_shared/Gpu : 0.75
Repeat : 2^4 Cpu execution : 159 ms Gpu execution : 6 ms Gpu_shared execution : 7 ms Gpu/Cpu : 26.50 Gpu_shared/Gpu : 0.86
Repeat : 2^5 Cpu execution : 310 ms Gpu execution : 12 ms Gpu_shared execution : 12 ms Gpu/Cpu : 25.83 Gpu_shared/Gpu : 1.00
Repeat : 2^6 Cpu execution : 620 ms Gpu execution : 22 ms Gpu_shared execution : 23 ms Gpu/Cpu : 28.18 Gpu_shared/Gpu : 0.96
Repeat : 2^7 Cpu execution : 1249 ms Gpu execution : 44 ms Gpu_shared execution : 44 ms Gpu/Cpu : 28.39 Gpu_shared/Gpu : 1.00
Repeat : 2^8 Cpu execution : 2487 ms Gpu execution : 87 ms Gpu_shared execution : 88 ms Gpu/Cpu : 28.59 Gpu_shared/Gpu : 0.99
Repeat : 2^9 Cpu execution : 4971 ms Gpu execution : 174 ms Gpu_shared execution : 163 ms Gpu/Cpu : 28.57 Gpu_shared/Gpu : 1.07
Repeat : 2^10 Cpu execution : 9935 ms Gpu execution : 271 ms Gpu_shared execution : 266 ms Gpu/Cpu : 36.66 Gpu_shared/Gpu : 1.02
编辑：我已在编译器选项中禁用了cuda优化，并且结果出奇地快。我也改变了
C[index] = A[index] + B[index];
到
C[index] += A[index] + B[index];
否则，共享内存在0-1毫秒内完成也将重复= 2 ^ 1000。我认为这是另一个问题。我已经用“ +”和“ + =”检查了C []数组的结果。 CPU，GPU，GPU_shared的结果相同。我认为这是另一个编译器问题。它不计算它的“重复”次数。如果我们添加“ + =”，则可以确定该程序将其计算为“重复”次数。新的代码在这里
#include <stdio.h>
#include <stdint.h>
#include <time.h>
#include <math.h>
#define threads_per_block 1024
#define blocks_per_grid 1024
#define shared_memory_size threads_per_block * 4 * 3
float clock_t1 = 0, clock_t2 = 0, clock_dt = 0;
void VectorAdd_cpu(float *A, float *B, float *C, int64_t n, int64_t repeat) {
uint32_t index = 0, j = 0;
for (index = 0; index < n; index++) {
j = 0;
while (j < repeat) {
j++;
C[index] += A[index] + B[index];
}
}
}
__global__ void VectorAdd_gpu(float *Ac, float *Bc, float *Cc, uint32_t n, uint32_t repeat) {
uint32_t index = blockIdx.x * blockDim.x + threadIdx.x;
uint32_t j;
if (index < n) {
j = 0;
while (j < repeat) {
j++;
Cc[index] += Ac[index] + Bc[index];
}
}
}
__global__ void VectorAdd_gpu_shared(float *Acs, float *Bcs, float *Ccs, uint32_t n, uint32_t repeat) {
uint32_t index = blockIdx.x * blockDim.x + threadIdx.x;
uint32_t tx = threadIdx.x;
uint32_t j;
float *Acs_temp, *Bcs_temp, *Ccs_temp;
extern __shared__ float S[];
Acs_temp = (float*)S;
Bcs_temp = (float*)Acs_temp + threads_per_block;
Ccs_temp = (float*)Bcs_temp + threads_per_block;
__syncthreads();
if (index < n) {
Acs_temp[tx] = Acs[index];
Bcs_temp[tx] = Bcs[index];
Ccs_temp[tx] = Ccs[index];
j = 0;
while (j < repeat) {
j++;
Ccs_temp[tx] += Acs_temp[tx] + Bcs_temp[tx];
}
Acs[index] = Acs_temp[tx];
Bcs[index] = Bcs_temp[tx];
Ccs[index] = Ccs_temp[tx];
}
}
int main() {
int64_t lenArray = threads_per_block * blocks_per_grid;
int64_t repeat = 0;
float *A, *B, *C, *Ac, *Bc, *Cc, *Acs, *Bcs, *Ccs;
uint64_t Size_array = lenArray * sizeof(float);
clock_t1 = float(clock());
A = (float *)malloc(Size_array);
B = (float *)malloc(Size_array);
C = (float *)malloc(Size_array);
clock_t2 = float(clock());printf("Cpu memory allocation : %.f ms\n", clock_t2 - clock_t1);
clock_t1 = float(clock());
cudaMallocManaged(&Ac, Size_array);
cudaMallocManaged(&Bc, Size_array);
cudaMallocManaged(&Cc, Size_array);
cudaMallocManaged(&Acs, Size_array);
cudaMallocManaged(&Bcs, Size_array);
cudaMallocManaged(&Ccs, Size_array);
clock_t2 = float(clock());printf("Gpu memory allocation : %.f ms\n", clock_t2 - clock_t1);
clock_t1 = float(clock());
for (int64_t i = 0; i < lenArray; i++) {
A[i] = i;
B[i] = i;
C[i] = 0;
}
clock_t2 = float(clock());printf("Cpu setting variable : %.f ms\n", clock_t2 - clock_t1);
clock_t1 = float(clock());printf("Cpu to Gpu copy variable :");
cudaMemcpy(Ac, A, Size_array, cudaMemcpyHostToDevice);
cudaMemcpy(Bc, B, Size_array, cudaMemcpyHostToDevice);
cudaMemcpy(Acs, A, Size_array, cudaMemcpyHostToDevice);
cudaMemcpy(Bcs, B, Size_array, cudaMemcpyHostToDevice);
clock_t2 = float(clock());printf("%.f ms\n", clock_t2 - clock_t1);
float temp1 = 0, temp2 = 0, temp3 = 0;
for (int i = 0;i <= 12;i++) {
repeat = pow(2, i);printf("Repeat : 2^%d\t", i);
clock_t1 = float(clock());printf("Cpu execution : ");
VectorAdd_cpu(A, B, C, lenArray, repeat);
clock_t2 = float(clock());printf("%.f\tms ", clock_t2 - clock_t1);
temp1 = clock_t2 - clock_t1;
clock_t1 = float(clock());printf("Gpu execution : ");
VectorAdd_gpu << <blocks_per_grid, threads_per_block >> > (Ac, Bc, Cc, lenArray, repeat);cudaDeviceSynchronize();
clock_t2 = float(clock());printf("%.f\tms ", clock_t2 - clock_t1);
temp2 = clock_t2 - clock_t1;
clock_t1 = float(clock());printf("Gpu_shared execution : ");
VectorAdd_gpu_shared << <blocks_per_grid, threads_per_block, shared_memory_size >> > (Acs, Bcs, Ccs, lenArray, repeat);cudaDeviceSynchronize();
clock_t2 = float(clock());printf("%.f\tms ", clock_t2 - clock_t1);
temp3 = clock_t2 - clock_t1;
printf("Gpu/Cpu : %.2f\t", temp1 / temp2);
printf("Gpu_shared/Gpu : %.2f\n", temp2 / temp3);
}
free(A);free(B);free(C);
cudaFree(Ac);cudaFree(Bc);cudaFree(Cc);
cudaFree(Acs);cudaFree(Bcs);cudaFree(Ccs);
printf("\n\nDone...\n\n");
return 1;
}
cpu的结果
Cpu memory allocation : 0 ms
Gpu memory allocation : 154 ms
Cpu setting variable : 5 ms
Cpu to Gpu copy variable :3 ms
Repeat : 2^0 Cpu execution : 3 ms Gpu execution : 1 ms Gpu_shared execution : 1 ms Gpu/Cpu : 3.00 Gpu_shared/Gpu : 1.00
Repeat : 2^1 Cpu execution : 6 ms Gpu execution : 1 ms Gpu_shared execution : 1 ms Gpu/Cpu : 6.00 Gpu_shared/Gpu : 1.00
Repeat : 2^2 Cpu execution : 11 ms Gpu execution : 1 ms Gpu_shared execution : 1 ms Gpu/Cpu : 11.00 Gpu_shared/Gpu : 1.00
Repeat : 2^3 Cpu execution : 22 ms Gpu execution : 2 ms Gpu_shared execution : 1 ms Gpu/Cpu : 11.00 Gpu_shared/Gpu : 2.00
Repeat : 2^4 Cpu execution : 45 ms Gpu execution : 3 ms Gpu_shared execution : 1 ms Gpu/Cpu : 15.00 Gpu_shared/Gpu : 3.00
Repeat : 2^5 Cpu execution : 89 ms Gpu execution : 2 ms Gpu_shared execution : 1 ms Gpu/Cpu : 44.50 Gpu_shared/Gpu : 2.00
Repeat : 2^6 Cpu execution : 178 ms Gpu execution : 2 ms Gpu_shared execution : 0 ms Gpu/Cpu : 89.00 Gpu_shared/Gpu : inf
Repeat : 2^7 Cpu execution : 357 ms Gpu execution : 2 ms Gpu_shared execution : 1 ms Gpu/Cpu : 178.50 Gpu_shared/Gpu : 2.00
Repeat : 2^8 Cpu execution : 718 ms Gpu execution : 5 ms Gpu_shared execution : 0 ms Gpu/Cpu : 143.60 Gpu_shared/Gpu : inf
Repeat : 2^9 Cpu execution : 1400 ms Gpu execution : 9 ms Gpu_shared execution : 1 ms Gpu/Cpu : 155.56 Gpu_shared/Gpu : 9.00
Repeat : 2^10 Cpu execution : 2790 ms Gpu execution : 17 ms Gpu_shared execution : 1 ms Gpu/Cpu : 164.12 Gpu_shared/Gpu : 17.00
Repeat : 2^11 Cpu execution : 5566 ms Gpu execution : 34 ms Gpu_shared execution : 2 ms Gpu/Cpu : 163.71 Gpu_shared/Gpu : 17.00
Repeat : 2^12 Cpu execution : 11124 ms Gpu execution : 67 ms Gpu_shared execution : 4 ms Gpu/Cpu : 166.03 Gpu_shared/Gpu : 16.75
Done...
我也没有cpu进行了测试，因为；太慢，无法清楚地看到差异。
//VectorAdd_cpu(A, B, C, lenArray, repeat);
Cpu memory allocation : 0 ms
Gpu memory allocation : 199 ms
Cpu setting variable : 6 ms
Cpu to Gpu copy variable :3 ms
Repeat : 2^0 Cpu execution : 0 ms Gpu execution : 0 ms Gpu_shared execution : 0 ms Gpu/Cpu : -nan(ind) Gpu_shared/Gpu : -nan(ind)
Repeat : 2^1 Cpu execution : 0 ms Gpu execution : 1 ms Gpu_shared execution : 0 ms Gpu/Cpu : 0.00 Gpu_shared/Gpu : inf
Repeat : 2^2 Cpu execution : 0 ms Gpu execution : 1 ms Gpu_shared execution : 0 ms Gpu/Cpu : 0.00 Gpu_shared/Gpu : inf
Repeat : 2^3 Cpu execution : 0 ms Gpu execution : 1 ms Gpu_shared execution : 0 ms Gpu/Cpu : 0.00 Gpu_shared/Gpu : inf
Repeat : 2^4 Cpu execution : 0 ms Gpu execution : 1 ms Gpu_shared execution : 0 ms Gpu/Cpu : 0.00 Gpu_shared/Gpu : inf
Repeat : 2^5 Cpu execution : 0 ms Gpu execution : 1 ms Gpu_shared execution : 0 ms Gpu/Cpu : 0.00 Gpu_shared/Gpu : inf
Repeat : 2^6 Cpu execution : 0 ms Gpu execution : 2 ms Gpu_shared execution : 0 ms Gpu/Cpu : 0.00 Gpu_shared/Gpu : inf
Repeat : 2^7 Cpu execution : 0 ms Gpu execution : 3 ms Gpu_shared execution : 0 ms Gpu/Cpu : 0.00 Gpu_shared/Gpu : inf
Repeat : 2^8 Cpu execution : 1 ms Gpu execution : 4 ms Gpu_shared execution : 1 ms Gpu/Cpu : 0.25 Gpu_shared/Gpu : 4.00
Repeat : 2^9 Cpu execution : 0 ms Gpu execution : 9 ms Gpu_shared execution : 1 ms Gpu/Cpu : 0.00 Gpu_shared/Gpu : 9.00
Repeat : 2^10 Cpu execution : 0 ms Gpu execution : 18 ms Gpu_shared execution : 1 ms Gpu/Cpu : 0.00 Gpu_shared/Gpu : 18.00
Repeat : 2^11 Cpu execution : 0 ms Gpu execution : 35 ms Gpu_shared execution : 2 ms Gpu/Cpu : 0.00 Gpu_shared/Gpu : 17.50
Repeat : 2^12 Cpu execution : 0 ms Gpu execution : 73 ms Gpu_shared execution : 4 ms Gpu/Cpu : 0.00 Gpu_shared/Gpu : 18.25
Repeat : 2^13 Cpu execution : 1 ms Gpu execution : 133 ms Gpu_shared execution : 6 ms Gpu/Cpu : 0.01 Gpu_shared/Gpu : 22.17
Repeat : 2^14 Cpu execution : 0 ms Gpu execution : 246 ms Gpu_shared execution : 11 ms Gpu/Cpu : 0.00 Gpu_shared/Gpu : 22.36
Repeat : 2^15 Cpu execution : 0 ms Gpu execution : 438 ms Gpu_shared execution : 20 ms Gpu/Cpu : 0.00 Gpu_shared/Gpu : 21.90
Repeat : 2^16 Cpu execution : 1 ms Gpu execution : 881 ms Gpu_shared execution : 39 ms Gpu/Cpu : 0.00 Gpu_shared/Gpu : 22.59
Repeat : 2^17 Cpu execution : 0 ms Gpu execution : 1768 ms Gpu_shared execution : 78 ms Gpu/Cpu : 0.00 Gpu_shared/Gpu : 22.67
Repeat : 2^18 Cpu execution : 0 ms Gpu execution : 3508 ms Gpu_shared execution : 158 ms Gpu/Cpu : 0.00 Gpu_shared/Gpu : 22.20
Repeat : 2^19 Cpu execution : 0 ms Gpu execution : 7068 ms Gpu_shared execution : 296 ms Gpu/Cpu : 0.00 Gpu_shared/Gpu : 23.88
Done...