济南品牌网站建设公司线下推广的渠道和方法

使用CUDA Graphs及更新Kernel参数示例

CUDA Graphs提供了一种高效的方式来捕获和执行一系列CUDA操作，特别适合重复执行相同操作序列的场景。下面我将展示如何使用CUDA Graphs以及如何更新Graph中的kernel参数。

基本CUDA Graphs使用示例

#include <stdio.h>
#include <cuda_runtime.h>#define N 1024__global__ void addKernel(int *a, int *b, int *c, int scale) {int i = blockIdx.x * blockDim.x + threadIdx.x;if (i < N) {c[i] = a[i] + b[i] * scale;}
}int main() {// 初始化主机数据int ha[N], hb[N], hc[N];for (int i = 0; i < N; i++) {ha[i] = i;hb[i] = i * 2;}// 分配设备内存int *da, *db, *dc;cudaMalloc(&da, N * sizeof(int));cudaMalloc(&db, N * sizeof(int));cudaMalloc(&dc, N * sizeof(int));// 拷贝数据到设备cudaMemcpy(da, ha, N * sizeof(int), cudaMemcpyHostToDevice);cudaMemcpy(db, hb, N * sizeof(int), cudaMemcpyHostToDevice);// 创建CUDA GraphcudaGraph_t graph;cudaGraphExec_t graphExec;cudaStream_t stream;cudaStreamCreate(&stream);// 开始捕获GraphcudaStreamBeginCapture(stream, cudaStreamCaptureModeGlobal);// 在流中执行操作(这些操作将被捕获到Graph中)dim3 block(256);dim3 grid((N + block.x - 1) / block.x);int scale = 1; // 初始参数值addKernel<<<grid, block, 0, stream>>>(da, db, dc, scale);// 结束捕获并实例化GraphcudaStreamEndCapture(stream, &graph);cudaGraphInstantiate(&graphExec, graph, NULL, NULL, 0);// 执行GraphcudaGraphLaunch(graphExec, stream);cudaStreamSynchronize(stream);// 检查结果cudaMemcpy(hc, dc, N * sizeof(int), cudaMemcpyDeviceToHost);printf("Result[0] = %d (should be 0)\n", hc[0]);printf("Result[1] = %d (should be 3)\n", hc[1]);// 更新Graph中的kernel参数并重新执行// 方法1: 重新捕获Graph(适用于参数或结构变化较大的情况)cudaGraphDestroy(graph);cudaGraphExecDestroy(graphExec);cudaStreamBeginCapture(stream, cudaStreamCaptureModeGlobal);scale = 2; // 更新参数值addKernel<<<grid, block, 0, stream>>>(da, db, dc, scale);cudaStreamEndCapture(stream, &graph);cudaGraphInstantiate(&graphExec, graph, NULL, NULL, 0);cudaGraphLaunch(graphExec, stream);cudaStreamSynchronize(stream);cudaMemcpy(hc, dc, N * sizeof(int), cudaMemcpyDeviceToHost);printf("After update:\n");printf("Result[0] = %d (should be 0)\n", hc[0]);printf("Result[1] = %d (should be 5)\n", hc[1]);// 方法2: 使用cudaGraphExecKernelNodeSetParams更新单个kernel参数(更高效)// 首先找到kernel节点cudaGraphNode_t* nodes = NULL;size_t numNodes = 0;cudaGraphGetNodes(graph, nodes, &numNodes); // 第一次获取节点数量nodes = (cudaGraphNode_t*)malloc(numNodes * sizeof(cudaGraphNode_t));cudaGraphGetNodes(graph, nodes, numNodes); // 第二次获取节点// 遍历节点找到kernel节点cudaGraphNode_t kernelNode = NULL;for (size_t i = 0; i < numNodes; i++) {cudaGraphNodeType type;cudaGraphNodeGetType(nodes[i], &type);if (type == cudaGraphNodeTypeKernel) {kernelNode = nodes[i];break;}}if (kernelNode) {// 设置新的kernel参数scale = 3;cudaKernelNodeParams params;memset(&params, 0, sizeof(params));params.func = (void*)addKernel;params.gridDim = grid;params.blockDim = block;params.sharedMemBytes = 0;params.kernelParams = (void**)&da, &db, &dc, &scale;params.extra = NULL;// 更新参数cudaGraphExecKernelNodeSetParams(graphExec, kernelNode, &params);// 再次执行cudaGraphLaunch(graphExec, stream);cudaStreamSynchronize(stream);cudaMemcpy(hc, dc, N * sizeof(int), cudaMemcpyDeviceToHost);printf("After cudaGraphExecKernelNodeSetParams:\n");printf("Result[0] = %d (should be 0)\n", hc[0]);printf("Result[1] = %d (should be 7)\n", hc[1]);}// 清理资源free(nodes);cudaGraphExecDestroy(graphExec);cudaGraphDestroy(graph);cudaStreamDestroy(stream);cudaFree(da);cudaFree(db);cudaFree(dc);return 0;
}

关键点说明

1. Graph捕获:

   • 使用cudaStreamBeginCapture和cudaStreamEndCapture捕获操作序列
   • 捕获期间在流上执行的所有操作都会被记录到Graph中

2. Graph执行:

   • 使用cudaGraphInstantiate创建可执行Graph实例
   • 使用cudaGraphLaunch执行Graph

3. 参数更新方法:

   • 方法1: 重新捕获整个Graph - 简单但效率较低
   • 方法2: 使用cudaGraphExecKernelNodeSetParams直接更新kernel参数 - 更高效但代码更复杂

4. 性能考虑:

   • CUDA Graphs最适合操作序列固定但参数可能变化的场景
   • 对于频繁变化的参数，使用方法2更新更高效
   • 如果操作结构变化很大，可能需要重新捕获Graph

编译运行

使用nvcc编译:

nvcc cuda_graph_example.cu -o cuda_graph_example
./cuda_graph_example

这个示例展示了CUDA Graphs的基本用法和参数更新技术，你可以根据实际需求调整kernel函数和参数更新逻辑。