Performance Evaluation of Heterogeneous GPU Programming Frameworks for Hemodynamic Simulations
Aristotle Martin, Geng Liu, William Ladd, Seyong Lee, John P. Gounley, Jeffrey S. Vetter, Saumil Patel, Silvio Rizzi, Victor A. Mateevitsi, Joseph A. Insley, Amanda Peters Randles · 2023
Preparing for the deployment of large scientific and engineering codes on upcoming exascale systems with GPU-dense nodes is made challenging by the unprecedented diversity of device architectures and heterogeneous programming models. In this work, we evaluate the process of porting a massively parallel, fluid dynamics code written in CUDA to SYCL, HIP, and Kokkos with a range of backends, using a combination of automated tools and manual tuning. We use a proxy application along with a custom performance model to inform the results and identify additional optimization strategies. At scale performance of the programming model implementations are evaluated on pre-production GPU node architectures for Frontier and Aurora, as well as on current NVIDIA device-based systems Summit and Polaris. Real-world workloads representing 3D blood flow calculations in complex vasculature are assessed. Our analysis highlights critical trade-offs between code performance, portability, and development time.