stream是什么
nivdia给出的解释是:
A sequence of operations that execute in issue-order on the GPU. 可以理解成在GPU上执行的操作序列.比如下面的这些动作.
cudaMemcpy()
kernel launch
device sync
cudaMemcpy()
不同的流操作可能是交叉执行的,可能是同事执行的.
流的API:
cudaEvent_t start;
cudaEventCreate(&start);
cudaEventRecord( start, 0 );
我们可以把一个应用程序的整体对的stream的情况称之为pipeline.优化程序以stream的角度就是优化pipeline
cuda overlap重叠
支持设备重叠的cuda GPU设备能够在执行kernel函数时同时执行设备与主机之间的内存拷贝动作.可以用下面的代码查看设备是否支持overlap:
int dev_count; cudaDeviceProp prop; cudaGetDeviceCount( &dev_count); for (int i = 0; i < dev_count; i++) { cudaGetDeviceProperties(&prop, i); if (prop.deviceOverlap) ...
cudaMemcpyAsync()
memcpy是以同步方式执行的,当函数返回时,复制操作已经完成.而cudaMemcpyAsync()是异步函数,它只是放置一个请求,表示在流中执行一次内存复制操作,这个复制操作是通过参数stream来指定的.当函数返回时我们无法保证函数已经执行完成,能够保证的是复制操作肯定会在下一个放入流的操作之前执行完成.任何传递给cudaMemcpyAsync()的主机内存指针都必须已经通过cudaHostAlloc()分配好内存,也就是,你只能以异步方式对页锁定内存进行复制操作.
Vector stream add 向量流加法
优化这个pipeline,最理想的pipeline如下:
可以看到在同一时间,lanuch kernel, copy host to device, copy device back to host 三个任务同时执行. 有2个stream流,一个是copy, 一个用于执行kernel.
实际优化pipeline的时候并不是这么简单和容易的,先看下面一段host代码:
for (int i=0; i<n; i+=SegSize*2) { cudaMemcpyAsync(d_A0, h_A+i, SegSize*sizeof(float),..., stream0); cudaMemcpyAsync(d_B0, h_B+i, SegSize*sizeof(float),..., stream0); vecAdd<<<SegSize/256, 256, 0, stream0>>>(d_A0, d_B0,...); cudaMemcpyAsync(h_C+i, d_C0, SegSize*sizeof(float),..., stream0); cudaMemcpyAsync(d_A1, h_A+i+SegSize, SegSize*sizeof(float),..., stream1); cudaMemcpyAsync(d_B1, h_B+i+SegSize, SegSize*sizeof(float) ,..., stream1); vecAdd<<<SegSize/256, 256, 0, stream1>>>(d_A1, d_B1, ...); cudaMemcpyAsync(d_C1, h_C+i+SegSize, SegSize*sizeof(float),..., stream1); }
这段代码的pipeline的情况是: 执行kernel计算和 下一块拷贝主机内存到设备是同事进行的.
再看下面这段代码:
for (int i=0; i<n; i+=SegSize*2) { cudaMemcpyAsync(d_A0, h_A+i, SegSize*sizeof(float),..., stream0); cudaMemcpyAsync(d_B0, h_B+i, SegSize*sizeof(float),..., stream0); cudaMemcpyAsync(d_A1, h_A+i+SegSize, SegSize*sizeof(float),..., stream1); cudaMemcpyAsync(d_B1, h_B+i+SegSize, SegSize*sizeof(float),..., stream1); vecAdd<<<SegSize/256, 256, 0, stream0>>>(d_A0, d_B0, ...); vecAdd<<<SegSize/256, 256, 0, stream1>>>(d_A1, d_B1, ...); cudaMemcpyAsync(h_C+i, d_C0, SegSize*sizeof(float),..., stream0); cudaMemcpyAsync(h_C+i+SegSize, d_C1, SegSize*sizeof(float),..., stream1); }
这段代码的pipeline情况是:和上一种的区别是把拷贝A和B元素与kernel并行,可以形象的理解成,下一行向左移动一下,那么整个pipeline整体是缩短了的.
strean 同步API
cudaStreamSynchronize(stream_id): 等待一个stream中的所有任务执行完成.
cudaDeviceSynchronize(): 不带参数等待设备中所有流任务执行完成
Vector-stream-add Code
首先使用2个stream来做:
#include <wb.h> #define wbCheck(stmt) do { cudaError_t err = stmt; if (err != cudaSuccess) { wbLog(ERROR, "Failed to run stmt ", #stmt); wbLog(ERROR, "Got CUDA error ... ", cudaGetErrorString(err)); return -1; } } while(0) #define SegSize 256 #define StreamNum 2 __global__ void vecAdd(float * in1, float * in2, float * out, int len) { //@@ Insert code to implement vector addition here int gidx = blockIdx.x*blockDim.x + threadIdx.x; if(gidx< len) { out[gidx]= in1[gidx]+in2[gidx]; } } int main(int argc, char ** argv) { wbArg_t args; int inputLength; float * hostInput1; float * hostInput2; float * hostOutput; // float * deviceInput1; // float * deviceInput2; // float * deviceOutput; float *h_A, *h_B, *h_C; //cudaStream_t stream0, stream1; //cudaStreamCreate(&stream0); //cudaStreamCreate(&stream1); float *d_A0, *d_B0, *d_C0;// device memory for stream 0 float *d_A1, *d_B1, *d_C1;// device memory for stream 1 args = wbArg_read(argc, argv); int Csize = SegSize*sizeof(float); wbTime_start(Generic, "Importing data and creating memory on host"); hostInput1 = (float *) wbImport(wbArg_getInputFile(args, 0), &inputLength); hostInput2 = (float *) wbImport(wbArg_getInputFile(args, 1), &inputLength); hostOutput = (float *) malloc(inputLength * sizeof(float)); printf("inputLength ==%d, SegSize =%d ", inputLength, SegSize); wbTime_stop(Generic, "Importing data and creating memory on host"); cudaHostAlloc((void**)&h_A, inputLength*sizeof(float), cudaHostAllocDefault); cudaHostAlloc((void**)&h_B, inputLength*sizeof(float), cudaHostAllocDefault); cudaHostAlloc((void**)&h_C, inputLength*sizeof(float), cudaHostAllocDefault); memcpy(h_A, hostInput1,inputLength*sizeof(float)); memcpy(h_B, hostInput2,inputLength*sizeof(float)); wbCheck(cudaMalloc((void **)&d_A0, Csize)); wbCheck(cudaMalloc((void **)&d_A1, Csize)); wbCheck(cudaMalloc((void **)&d_B0, Csize)); wbCheck(cudaMalloc((void **)&d_B1, Csize)); wbCheck(cudaMalloc((void **)&d_C0, Csize)); wbCheck(cudaMalloc((void **)&d_C1, Csize)); cudaStream_t *streams = (cudaStream_t*) malloc(StreamNum * sizeof(cudaStream_t)); for(int i = 0; i < StreamNum; i++) cudaStreamCreate(&(streams[i])); int main = inputLength/(SegSize*StreamNum); int left = inputLength%(SegSize*StreamNum); printf("main =%d, left=%d ", main, left); int i = 0; // keep the increaing length for(i; i < inputLength; i+=SegSize*StreamNum) { cudaMemcpyAsync(d_A0, hostInput1+i, Csize, cudaMemcpyHostToDevice, streams[0]); cudaMemcpyAsync(d_B0, hostInput2+i, Csize, cudaMemcpyHostToDevice, streams[0]); cudaMemcpyAsync(d_A1, hostInput1+i+SegSize, Csize, cudaMemcpyHostToDevice, streams[1]); cudaMemcpyAsync(d_B1, hostInput2+i+SegSize, Csize, cudaMemcpyHostToDevice, streams[1]); // block size is 256 vecAdd<<<SegSize/256, SegSize, 1, streams[0]>>>(d_A0, d_B0, d_C0, SegSize); vecAdd<<<SegSize/256, SegSize, 1, streams[1]>>>(d_A1, d_B1, d_C1, SegSize); // cudaStreamSynchronize(yiming_stream0); cudaMemcpyAsync(hostOutput+i, d_C0, Csize, cudaMemcpyDeviceToHost, streams[0]); //cudaStreamSynchronize(yiming_stream1); cudaMemcpyAsync(hostOutput+i+SegSize, d_C1, Csize, cudaMemcpyDeviceToHost, streams[1]); } // Process the remaining elements if(SegSize < left) { printf("AAAAAAA, left- size ==%d ", left-SegSize); cudaMemcpyAsync(d_A0, hostInput1+i, Csize, cudaMemcpyHostToDevice, streams[0]); cudaMemcpyAsync(d_B0, hostInput2+i, Csize, cudaMemcpyHostToDevice, streams[0]); cudaMemcpyAsync(d_A1, hostInput1+i+SegSize, (left-SegSize)*sizeof(float), cudaMemcpyHostToDevice, streams[1]); cudaMemcpyAsync(d_B1, hostInput2+i+SegSize, (left-SegSize)*sizeof(float), cudaMemcpyHostToDevice, streams[1]); // block size is 256 vecAdd<<<1, SegSize, 1, streams[0]>>>(d_A0, d_B0, d_C0, SegSize); vecAdd<<<1, left-SegSize, 1, streams[1]>>>(d_A0, d_B0, d_C0, left-SegSize); // cudaStreamSynchronize(streams[0]); cudaMemcpyAsync(hostOutput+i, d_C0, Csize,cudaMemcpyDeviceToHost, streams[0]); cudaMemcpyAsync(hostOutput+i+SegSize, d_C0, (left-SegSize)*sizeof(float),cudaMemcpyDeviceToHost, streams[1]); // i+=SegSize; // left = left - SegSize; } else if(left > 0) { printf("BBBBBBB "); cudaMemcpyAsync(d_A0, hostInput1+i, left*sizeof(float), cudaMemcpyHostToDevice); cudaMemcpyAsync(d_B0, hostInput2+i, left*sizeof(float), cudaMemcpyHostToDevice); vecAdd<<<1, left, 1, streams[0]>>>(d_A0, d_B0, d_C0, left); //cudaDeviceSynchronize(); cudaMemcpyAsync(hostOutput+i, d_C0, left*sizeof(float), cudaMemcpyDeviceToHost); } cudaDeviceSynchronize(); wbSolution(args, hostOutput, inputLength); free(hostInput1); free(hostInput2); free(hostOutput); for(int i = 0; i < StreamNum; i++) cudaStreamDestroy(streams[i]); cudaFree(d_A0); cudaFree(d_A1); cudaFree(d_B0); cudaFree(d_B1); cudaFree(d_C0); cudaFree(d_C1); return 0; }
然后是使用4个流来做,code如下:
#include <wb.h> #define wbCheck(stmt) do { cudaError_t err = stmt; if (err != cudaSuccess) { wbLog(ERROR, "Failed to run stmt ", #stmt); wbLog(ERROR, "Got CUDA error ... ", cudaGetErrorString(err)); return -1; } } while(0) #define SegSize 256 #define StreamNum 4 __global__ void vecAdd(float * in1, float * in2, float * out, int len) { //@@ Insert code to implement vector addition here int gidx = blockIdx.x*blockDim.x + threadIdx.x; if(gidx< len) { out[gidx]= in1[gidx]+in2[gidx]; } } int main(int argc, char ** argv) { wbArg_t args; int inputLength, i; float * hostInput1; float * hostInput2; float * hostOutput; // float * deviceInput1; // float * deviceInput2; // float * deviceOutput; float *h_A, *h_B, *h_C; //cudaStream_t stream0, stream1; //cudaStreamCreate(&stream0); //cudaStreamCreate(&stream1); float *d_A0, *d_B0, *d_C0;// device memory for stream 0 float *d_A1, *d_B1, *d_C1;// device memory for stream 1 float *d_A2, *d_B2, *d_C2;// device memory for stream 2 float *d_A3, *d_B3, *d_C3;// device memory for stream 3 args = wbArg_read(argc, argv); int Csize = SegSize*sizeof(float); wbTime_start(Generic, "Importing data and creating memory on host"); hostInput1 = (float *) wbImport(wbArg_getInputFile(args, 0), &inputLength); hostInput2 = (float *) wbImport(wbArg_getInputFile(args, 1), &inputLength); hostOutput = (float *) malloc(inputLength * sizeof(float)); printf("inputLength ==%d, SegSize =%d ", inputLength, SegSize); wbTime_stop(Generic, "Importing data and creating memory on host"); cudaHostAlloc((void**)&h_A, inputLength*sizeof(float), cudaHostAllocDefault); cudaHostAlloc((void**)&h_B, inputLength*sizeof(float), cudaHostAllocDefault); cudaHostAlloc((void**)&h_C, inputLength*sizeof(float), cudaHostAllocDefault); memcpy(h_A, hostInput1,inputLength*sizeof(float)); memcpy(h_B, hostInput2,inputLength*sizeof(float)); wbCheck(cudaMalloc((void **)&d_A0, Csize)); wbCheck(cudaMalloc((void **)&d_A1, Csize)); wbCheck(cudaMalloc((void **)&d_B0, Csize)); wbCheck(cudaMalloc((void **)&d_B1, Csize)); wbCheck(cudaMalloc((void **)&d_C0, Csize)); wbCheck(cudaMalloc((void **)&d_C1, Csize)); wbCheck(cudaMalloc((void **)&d_A2, Csize)); wbCheck(cudaMalloc((void **)&d_A3, Csize)); wbCheck(cudaMalloc((void **)&d_B2, Csize)); wbCheck(cudaMalloc((void **)&d_B3, Csize)); wbCheck(cudaMalloc((void **)&d_C2, Csize)); wbCheck(cudaMalloc((void **)&d_C3, Csize)); cudaStream_t *streams = (cudaStream_t*) malloc(StreamNum * sizeof(cudaStream_t)); for(int i = 0; i < StreamNum; i++) cudaStreamCreate(&(streams[i])); int main = inputLength/(SegSize*StreamNum); int left = inputLength%(SegSize*StreamNum); printf("main =%d, left=%d ", main, left); for(i=0; i < inputLength; i+=SegSize*StreamNum) { cudaMemcpyAsync(d_A0, hostInput1+i, Csize, cudaMemcpyHostToDevice, streams[0]); cudaMemcpyAsync(d_B0, hostInput2+i, Csize, cudaMemcpyHostToDevice, streams[0]); cudaMemcpyAsync(d_A1, hostInput1+i+SegSize, Csize, cudaMemcpyHostToDevice, streams[1]); cudaMemcpyAsync(d_B1, hostInput2+i+SegSize, Csize, cudaMemcpyHostToDevice, streams[1]); cudaMemcpyAsync(d_A2, hostInput1+i+SegSize*2, Csize, cudaMemcpyHostToDevice, streams[2]); cudaMemcpyAsync(d_B2, hostInput2+i+SegSize*2, Csize, cudaMemcpyHostToDevice, streams[2]); cudaMemcpyAsync(d_A3, hostInput1+i+SegSize*3, Csize, cudaMemcpyHostToDevice, streams[3]); cudaMemcpyAsync(d_B3, hostInput2+i+SegSize*3, Csize, cudaMemcpyHostToDevice, streams[3]); // block size is 256 vecAdd<<<SegSize/256, SegSize, 1, streams[0]>>>(d_A0, d_B0, d_C0, SegSize); vecAdd<<<SegSize/256, SegSize, 1, streams[1]>>>(d_A1, d_B1, d_C1, SegSize); vecAdd<<<SegSize/256, SegSize, 1, streams[2]>>>(d_A2, d_B2, d_C2, SegSize); vecAdd<<<SegSize/256, SegSize, 1, streams[3]>>>(d_A3, d_B3, d_C3, SegSize); cudaMemcpyAsync(hostOutput+i, d_C0, Csize, cudaMemcpyDeviceToHost, streams[0]); //cudaStreamSynchronize(yiming_stream1); cudaMemcpyAsync(hostOutput+i+SegSize, d_C1, Csize, cudaMemcpyDeviceToHost, streams[1]); cudaMemcpyAsync(hostOutput+i+SegSize*2, d_C2, Csize, cudaMemcpyDeviceToHost, streams[2]); cudaMemcpyAsync(hostOutput+i+SegSize*3, d_C3, Csize, cudaMemcpyDeviceToHost, streams[3]); } // Process the remaining elements if(SegSize*3 < left){ printf("DDDDDDDD "); cudaMemcpyAsync(d_A0, hostInput1+i, Csize, cudaMemcpyHostToDevice, streams[0]); cudaMemcpyAsync(d_B0, hostInput2+i, Csize, cudaMemcpyHostToDevice, streams[0]); cudaMemcpyAsync(d_A1, hostInput1+i+SegSize, Csize, cudaMemcpyHostToDevice, streams[1]); cudaMemcpyAsync(d_B1, hostInput2+i+SegSize, Csize, cudaMemcpyHostToDevice, streams[1]); cudaMemcpyAsync(d_A2, hostInput1+i+SegSize*2, Csize, cudaMemcpyHostToDevice, streams[2]); cudaMemcpyAsync(d_B2, hostInput2+i+SegSize*2, Csize, cudaMemcpyHostToDevice, streams[2]); cudaMemcpyAsync(d_A3, hostInput1+i+SegSize*3, (left-SegSize*3)*sizeof(float), cudaMemcpyHostToDevice, streams[3]); cudaMemcpyAsync(d_B3, hostInput2+i+SegSize*3, (left-SegSize*3)*sizeof(float), cudaMemcpyHostToDevice, streams[3]); // block size is 256 vecAdd<<<1, SegSize, 1, streams[0]>>>(d_A0, d_B0, d_C0, SegSize); vecAdd<<<1, SegSize, 1, streams[1]>>>(d_A1, d_B1, d_C1, SegSize); vecAdd<<<1, SegSize, 1, streams[2]>>>(d_A2, d_B2, d_C2, SegSize); vecAdd<<<1, (left-SegSize*3), 1, streams[3]>>>(d_A3, d_B3, d_C3, (left-SegSize*3)); cudaMemcpyAsync(hostOutput+i, d_C0, Csize, cudaMemcpyDeviceToHost, streams[0]); //cudaStreamSynchronize(yiming_stream1); cudaMemcpyAsync(hostOutput+i+SegSize, d_C1, Csize, cudaMemcpyDeviceToHost, streams[1]); cudaMemcpyAsync(hostOutput+i+SegSize*2, d_C2, Csize, cudaMemcpyDeviceToHost, streams[2]); cudaMemcpyAsync(hostOutput+i+SegSize*3, d_C3, (left-SegSize*3)*sizeof(float), cudaMemcpyDeviceToHost, streams[3]); } else if(SegSize*2 < left){ printf("CCCCCCCC "); cudaMemcpyAsync(d_A0, hostInput1+i, Csize, cudaMemcpyHostToDevice, streams[0]); cudaMemcpyAsync(d_B0, hostInput2+i, Csize, cudaMemcpyHostToDevice, streams[0]); cudaMemcpyAsync(d_A1, hostInput1+i+SegSize, Csize, cudaMemcpyHostToDevice, streams[1]); cudaMemcpyAsync(d_B1, hostInput2+i+SegSize, Csize, cudaMemcpyHostToDevice, streams[1]); cudaMemcpyAsync(d_A2, hostInput1+i+SegSize*2, (left-SegSize*2)*sizeof(float), cudaMemcpyHostToDevice, streams[2]); cudaMemcpyAsync(d_B2, hostInput2+i+SegSize*2, (left-SegSize*2)*sizeof(float), cudaMemcpyHostToDevice, streams[2]); // block size is 256 vecAdd<<<1, SegSize, 1, streams[0]>>>(d_A0, d_B0, d_C0, SegSize); vecAdd<<<1, SegSize, 1, streams[1]>>>(d_A1, d_B1, d_C1, SegSize); vecAdd<<<1, left-SegSize*2, 1, streams[2]>>>(d_A2, d_B2, d_C2, (left-SegSize*2)); cudaMemcpyAsync(hostOutput+i, d_C0, Csize, cudaMemcpyDeviceToHost, streams[0]); //cudaStreamSynchronize(yiming_stream1); cudaMemcpyAsync(hostOutput+i+SegSize, d_C1, Csize, cudaMemcpyDeviceToHost, streams[1]); cudaMemcpyAsync(hostOutput+i+SegSize*2, d_C2, (left-SegSize*2)*sizeof(float), cudaMemcpyDeviceToHost, streams[2]); } else if(SegSize < left) { printf("AAAAAAA, left- size ==%d ", left-SegSize); cudaMemcpyAsync(d_A0, hostInput1+i, Csize, cudaMemcpyHostToDevice, streams[0]); cudaMemcpyAsync(d_B0, hostInput2+i, Csize, cudaMemcpyHostToDevice, streams[0]); cudaMemcpyAsync(d_A1, hostInput1+i+SegSize, (left-SegSize)*sizeof(float), cudaMemcpyHostToDevice, streams[1]); cudaMemcpyAsync(d_B1, hostInput2+i+SegSize, (left-SegSize)*sizeof(float), cudaMemcpyHostToDevice, streams[1]); // block size is 256 vecAdd<<<1, SegSize, 1, streams[0]>>>(d_A0, d_B0, d_C0, SegSize); vecAdd<<<1, left-SegSize, 1, streams[1]>>>(d_A0, d_B0, d_C0, left- SegSize); // cudaStreamSynchronize(streams[0]); cudaMemcpyAsync(hostOutput+i, d_C0, Csize,cudaMemcpyDeviceToHost, streams[0]); cudaMemcpyAsync(hostOutput+i+SegSize, d_C1, (left-SegSize)*sizeof(float),cudaMemcpyDeviceToHost, streams[1]); // i+=SegSize; // left = left - SegSize; } else if(left > 0) { printf("BBBBBBB "); cudaMemcpyAsync(d_A0, hostInput1+i, left*sizeof(float), cudaMemcpyHostToDevice); cudaMemcpyAsync(d_B0, hostInput2+i, left*sizeof(float), cudaMemcpyHostToDevice); vecAdd<<<1, left, 1, streams[0]>>>(d_A0, d_B0, d_C0, left); //cudaDeviceSynchronize(); cudaMemcpyAsync(hostOutput+i, d_C0, left*sizeof(float), cudaMemcpyDeviceToHost); } cudaDeviceSynchronize(); wbSolution(args, hostOutput, inputLength); free(hostInput1); free(hostInput2); free(hostOutput); for(int i = 0; i < StreamNum; i++) cudaStreamDestroy(streams[i]); cudaFree(d_A0); cudaFree(d_A1); cudaFree(d_B0); cudaFree(d_B1); cudaFree(d_C0); cudaFree(d_C1); cudaFree(d_A2); cudaFree(d_A3); cudaFree(d_B2); cudaFree(d_B3); cudaFree(d_C2); cudaFree(d_C3); return 0; }
运行成功,但是遗留一个问题,当我把拷贝内存的代码改成:
cudaMemcpyAsync(d_A0, h_A+i, Csize, cudaMemcpyHostToDevice, streams[0]); 即使用页固定内存,结果就会错误,不明白为什么