Year: 2025
Type: conference-paper
Abstract: Portability is critical to ensuring high productivity in developing and maintaining scientific software as the diversity in on-node hardware architectures increases.While several programming models provide portability for diverse GPU systems, they don't make any guarantees about performance portability.In this work, we explore several programming models -CUDA, HIP, Kokkos, RAJA, OpenMP, OpenACC, and SYCL, to assess the consistency of their performance across NVIDIA and AMD GPUs.We use five proxy applications from different scientific domains, create implementations where missing, and use them to present a comprehensive comparative evaluation of the performance portability of these programming models.We provide a Spack scriptingbased methodology to ensure reproducibility of experiments conducted in this work.Finally, we analyze the reasons for why some programming models underperform in certain scenarios and in some cases, present performance optimizations to the proxy applications.
Authors: Joshua Hoke Davis, Pranav Sivaraman, Joy Kitson, Konstantinos Parasyris, Harshitha Menon, Isaac Minn, Giorgis Georgakoudis, Abhinav Bhatele
Language: English
Subfield: Hardware and Architecture
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Awards: G1079818582, G1088636562, G1143651532, G121915436, G1286236842, G1356433355, G14554683, G1645119126, G1677143136, G1799333409, G1863003419, G2145270357, G2411683797, G2988778103, CAREER: Self-tuning Parallel Software and Systems, G3083819904, G3136028036, G3571461640, G3669113783, G3945850812, G3976335900, G4017386982, G4094752214, G419881185, G4423657506, G4501827968, G498139845, G5076365615, G5685548091, G574795485, G6086734019, Graduate Research Fellowship Program (GRFP), G6339381329, G660579501, G6652958823, G7354628648, G7408511322, G7651947074, G8269158468, G8326222658, G8906985441, G895790057, G969889393, G991281326
OpenAlex ID: W4413412047