Beginner Explanation
Imagine you’re a chef in a busy restaurant. You have multiple dishes to prepare, but instead of cooking each dish one by one, you create a plan that outlines all the steps for each dish at once. This plan allows you to move between tasks efficiently, like chopping vegetables while waiting for water to boil. CUDAGraphs work similarly for a GPU. They allow programmers to create a plan of multiple tasks (operations) that the GPU can execute all at once, making it faster and more efficient, just like our chef in the kitchen.Technical Explanation
CUDAGraphs are a feature in CUDA that enables the recording and execution of a sequence of operations as a single entity. This is particularly useful for reducing the overhead associated with launching multiple kernels. By using CUDAGraphs, you can capture a series of CUDA operations, including kernel launches and memory transfers, and replay them efficiently. Here’s an example: “`c++ #includeAcademic Context
CUDAGraphs were introduced to address the performance bottlenecks associated with launching multiple kernels in CUDA applications. The concept is grounded in the need for optimizing GPU resource utilization and minimizing kernel launch overhead. Key papers discussing GPU programming models, such as ‘CUDA: A Parallel Computing Platform and Programming Model’ by Nickolls et al. (2008), provide foundational insights into GPU architecture and programming. Recent advancements in CUDAGraphs can be found in NVIDIA’s official documentation and research papers that explore the efficiency of graph-based execution models in high-performance computing.Code Examples
Example 1:
cudaGraph_t graph;
cudaGraphCreate(&graph, 0);Example 2:
cudaGraphNode_t kernelNode;
cudaKernelNodeParams kernelParams = {0};
kernelParams.func = (void*)myKernel;Example 3:
// Add kernel launch to the graph
cudaGraphAddKernelNode(&kernelNode, graph, nullptr, 0, &kernelParams);Example 4:
// Instantiate the graph for execution
cudaGraphLaunch(graph, 0);
cudaGraphDestroy(graph);
return 0;View Source: https://arxiv.org/abs/2511.16665v1