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An Adaptive Kernel Execution for Dynamic Applications on GPUs Using CUDA Graphs

SessionResearch, ACM SRC, and Doctoral Showcase Posters

DescriptionWe propose a novel approach for executing dynamic applications on GPUs. Different from traditional approaches that use a single kernel, our method allows the GPU to autonomously allocate computational resources at runtime. We decompose a kernel into multiple fragment kernels and dynamically launch an optimal number of them during execution. The input data is partitioned into smaller segments and each fragmented kernel processes each of the partitioned segments. This method is implemented using CUDA graphs conditional nodes for determining the number of fragmented kernels to be launched based on the input size. We compared the proposed method with the traditional kernel execution method with a Breadth-First Search (BFS) application, a representative dynamic application. Results show comparable performance while reducing utilization of compute resources by up to 19.9%, and opportunities to further performance improvement by optimizing parameters of our method.

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