cuda-graphs

cuda-graphs

You are cuda-graphs - a specialized skill for CUDA Graph capture and optimization. This skill provides expert capabilities for reducing kernel launch overhead and optimizing execution patterns through graph-based workflows.

Overview

This skill enables AI-powered CUDA Graph operations including:

• Capturing CUDA operations into graphs
• Instantiating and executing graph instances
• Updating graph node parameters
• Profiling graph vs stream execution
• Designing graph-friendly kernel patterns
• Handling conditional graph execution
• Integrating graphs with NCCL operations
• Optimizing launch latency for inference

Prerequisites

• NVIDIA CUDA Toolkit 10.0+ (basic graphs)
• CUDA 11.0+ for graph updates
• CUDA 12.0+ for conditional nodes
• GPU with compute capability 7.0+
• Nsight Systems for graph profiling

Capabilities

1. Stream Capture Basic

Capture stream operations into a graph:

#include <cuda_runtime.h>

cudaGraph_t graph;
cudaGraphExec_t graphExec;
cudaStream_t stream;

cudaStreamCreate(&stream);

// Begin stream capture
cudaStreamBeginCapture(stream, cudaStreamCaptureModeGlobal);

// Record operations to be captured
kernel1<<<grid1, block1, 0, stream>>>(args1);
kernel2<<<grid2, block2, 0, stream>>>(args2);
kernel3<<<grid3, block3, 0, stream>>>(args3);

// End capture and create graph
cudaStreamEndCapture(stream, &graph);

// Instantiate the graph for execution
cudaGraphInstantiate(&graphExec, graph, NULL, NULL, 0);

// Execute the graph (much lower overhead than individual launches)
for (int i = 0; i < iterations; i++) {
    cudaGraphLaunch(graphExec, stream);
}
cudaStreamSynchronize(stream);

// Cleanup
cudaGraphExecDestroy(graphExec);
cudaGraphDestroy(graph);
cudaStreamDestroy(stream);

2. Explicit Graph Construction

Build graphs programmatically:

cudaGraph_t graph;
cudaGraphCreate(&graph, 0);

// Create kernel nodes
cudaKernelNodeParams kernelParams1 = {0};
kernelParams1.func = (void*)kernel1;
kernelParams1.gridDim = grid1;
kernelParams1.blockDim = block1;
kernelParams1.sharedMemBytes = 0;
kernelParams1.kernelParams = kernelArgs1;

cudaKernelNodeParams kernelParams2 = {0};
kernelParams2.func = (void*)kernel2;
kernelParams2.gridDim = grid2;
kernelParams2.blockDim = block2;
kernelParams2.sharedMemBytes = 0;
kernelParams2.kernelParams = kernelArgs2;

cudaGraphNode_t node1, node2;

// Add first kernel (no dependencies)
cudaGraphAddKernelNode(&node1, graph, NULL, 0, &kernelParams1);

// Add second kernel (depends on first)
cudaGraphNode_t dependencies[] = {node1};
cudaGraphAddKernelNode(&node2, graph, dependencies, 1, &kernelParams2);

// Instantiate and execute
cudaGraphExec_t graphExec;
cudaGraphInstantiate(&graphExec, graph, NULL, NULL, 0);
cudaGraphLaunch(graphExec, stream);

3. Graph Node Types

// Memory copy node
cudaMemcpy3DParms copyParams = {0};
// ... configure copy parameters
cudaGraphNode_t copyNode;
cudaGraphAddMemcpyNode(&copyNode, graph, NULL, 0, &copyParams);

// Memset node
cudaMemsetParams memsetParams = {0};
memsetParams.dst = d_array;
memsetParams.value = 0;
memsetParams.pitch = 0;
memsetParams.elementSize = sizeof(int);
memsetParams.width = N;
memsetParams.height = 1;
cudaGraphNode_t memsetNode;
cudaGraphAddMemsetNode(&memsetNode, graph, NULL, 0, &memsetParams);

// Host function node
cudaHostNodeParams hostParams = {0};
hostParams.fn = hostCallback;
hostParams.userData = userData;
cudaGraphNode_t hostNode;
cudaGraphAddHostNode(&hostNode, graph, dependencies, numDeps, &hostParams);

// Event record/wait nodes (CUDA 11.1+)
cudaEvent_t event;
cudaEventCreate(&event);
cudaGraphNode_t eventRecordNode, eventWaitNode;
cudaGraphAddEventRecordNode(&eventRecordNode, graph, deps, numDeps, event);
cudaGraphAddEventWaitNode(&eventWaitNode, graph, deps, numDeps, event);

// Empty node (for dependencies only)
cudaGraphNode_t emptyNode;
cudaGraphAddEmptyNode(&emptyNode, graph, deps, numDeps);

4. Graph Updates (CUDA 11+)

Update graph parameters without rebuilding:

cudaGraph_t graph;
cudaGraphExec_t graphExec;

// Initial capture
cudaStreamBeginCapture(stream, cudaStreamCaptureModeGlobal);
myKernel<<<grid, block, 0, stream>>>(d_input, d_output, N);
cudaStreamEndCapture(stream, &graph);
cudaGraphInstantiate(&graphExec, graph, NULL, NULL, 0);

// Execute initial graph
cudaGraphLaunch(graphExec, stream);
cudaStreamSynchronize(stream);

// Update the graph with new capture
cudaGraph_t newGraph;
cudaStreamBeginCapture(stream, cudaStreamCaptureModeGlobal);
myKernel<<<grid, block, 0, stream>>>(d_input2, d_output2, N);  // Different pointers
cudaStreamEndCapture(stream, &newGraph);

// Update executable graph
cudaGraphExecUpdateResult updateResult;
cudaGraphExecUpdate(graphExec, newGraph, NULL, &updateResult);

if (updateResult == cudaGraphExecUpdateSuccess) {
    // Graph updated successfully
    cudaGraphLaunch(graphExec, stream);
} else {
    // Need to reinstantiate
    cudaGraphExecDestroy(graphExec);
    cudaGraphInstantiate(&graphExec, newGraph, NULL, NULL, 0);
    cudaGraphLaunch(graphExec, stream);
}

cudaGraphDestroy(newGraph);

5. Kernel Node Parameter Updates

// Get kernel node from graph
cudaGraphNode_t* nodes;
size_t numNodes;
cudaGraphGetNodes(graph, NULL, &numNodes);
nodes = new cudaGraphNode_t[numNodes];
cudaGraphGetNodes(graph, nodes, &numNodes);

// Find and update kernel node
for (size_t i = 0; i < numNodes; i++) {
    cudaGraphNodeType nodeType;
    cudaGraphNodeGetType(nodes[i], &nodeType);

    if (nodeType == cudaGraphNodeTypeKernel) {
        cudaKernelNodeParams params;
        cudaGraphKernelNodeGetParams(nodes[i], &params);

        // Update parameters
        void* newArgs[] = {&newInput, &newOutput, &newN};
        params.kernelParams = newArgs;

        // Set new parameters
        cudaGraphExecKernelNodeSetParams(graphExec, nodes[i], &params);
    }
}

delete[] nodes;

6. Graph Performance Benchmarking

void benchmarkGraphVsStreams(int numKernels, int iterations) {
    cudaEvent_t start, stop;
    cudaEventCreate(&start);
    cudaEventCreate(&stop);

    // Benchmark stream-based execution
    cudaEventRecord(start);
    for (int i = 0; i < iterations; i++) {
        for (int k = 0; k < numKernels; k++) {
            smallKernel<<<grid, block, 0, stream>>>(d_data, N);
        }
    }
    cudaEventRecord(stop);
    cudaEventSynchronize(stop);

    float streamTime;
    cudaEventElapsedTime(&streamTime, start, stop);

    // Benchmark graph-based execution
    cudaGraph_t graph;
    cudaStreamBeginCapture(stream, cudaStreamCaptureModeGlobal);
    for (int k = 0; k < numKernels; k++) {
        smallKernel<<<grid, block, 0, stream>>>(d_data, N);
    }
    cudaStreamEndCapture(stream, &graph);

    cudaGraphExec_t graphExec;
    cudaGraphInstantiate(&graphExec, graph, NULL, NULL, 0);

    cudaEventRecord(start);
    for (int i = 0; i < iterations; i++) {
        cudaGraphLaunch(graphExec, stream);
    }
    cudaEventRecord(stop);
    cudaEventSynchronize(stop);

    float graphTime;
    cudaEventElapsedTime(&graphTime, start, stop);

    printf("Stream execution: %.3f ms\n", streamTime);
    printf("Graph execution:  %.3f ms\n", graphTime);
    printf("Speedup: %.2fx\n", streamTime / graphTime);

    cudaGraphExecDestroy(graphExec);
    cudaGraphDestroy(graph);
}

7. Inference Pipeline with Graphs

class InferenceGraphPipeline {
private:
    cudaGraph_t graph;
    cudaGraphExec_t graphExec;
    cudaStream_t stream;

    // Model weights (constant during inference)
    float* d_weights1;
    float* d_weights2;

    // Buffers (reused per inference)
    float* d_input;
    float* d_hidden;
    float* d_output;

public:
    void initGraph() {
        cudaStreamCreate(&stream);

        // Capture inference operations
        cudaStreamBeginCapture(stream, cudaStreamCaptureModeGlobal);

        // Layer 1: Input -> Hidden
        matmulKernel<<<grid1, block1, 0, stream>>>(d_input