Performance Evaluation of Heterogeneous GPU Programming Frameworks for Hemodynamic Simulations
- Duke University
- Argonne National Laboratory
- ORNL
- Argonne National Laboratory (ANL)
- Duke university Duhram, NC
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.
- Research Organization:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC05-00OR22725
- OSTI ID:
- 2224199
- Resource Relation:
- Conference: 2023 International Workshop on Performance, Portability & Productivity in HPC - Denver, Colorado, United States of America - 11/12/2023 10:00:00 AM-11/17/2023 10:00:00 AM
- Country of Publication:
- United States
- Language:
- English
Similar Records
Cabana: A Performance Portable Library for Particle-Based Simulations
Studying Performance Portability of LAMMPS across Diverse GPU-based Platforms