The International Conference for High Performance Computing, Networking, Storage, and Analysis
The International Conference for High Performance Computing, Networking, Storage, and Analysis
Student: Aristotle Martin (Duke University)
Supervisor: Aristotle Martin (Duke University)
Abstract: Preparing for the deployment of large scientific and engineering codes on GPU-dense exascale systems is made challenging by the unprecedented diversity of vendor hardware and programming model alternatives for offload acceleration. To leverage the exaflops of GPUs from Frontier (AMD) and Aurora (Intel), users of high performance computing (HPC) legacy codes originally written to target NVIDIA GPUs will have to make decisions with implications regarding porting effort, performance, and code maintainability. To facilitate HPC users navigating this space, we have established a pipeline that combines generalized GPU performance models with proxy applications to evaluate the performance portability of a massively parallel computational fluid dynamics (CFD) code in CUDA, SYCL, HIP, and Kokkos with backends on current NVIDIA-based machines as well as testbeds for Aurora (Intel) and Frontier (AMD). We demonstrate the utility of predictive models and proxy applications in gauging performance bounds and guiding hand-tuning efforts.
ACM-SRC Semi-Finalist: no
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