Assessing Application Efficiency and Performance Portability in Single-Source Programming for Heterogeneous Parallel Systems
PELAB, Department of Computer and Information Science, Linköping University, Linköping, Sweden
International Journal of Parallel Programming, 2022
We analyze the performance portability of the skeleton-based, single-source multi-backend high-level programming framework SkePU across multiple different CPU–GPU heterogeneous systems. Thereby, we provide a systematic application efficiency characterization of SkePU-generated code in comparison to equivalent hand-written code in more low-level parallel programming models such as OpenMP and CUDA. For this purpose, we contribute ports of the STREAM benchmark suite and of a part of the NAS Parallel Benchmark suite to SkePU. We show that for STREAM and the EP benchmark, SkePU regularly scores efficiency values above 80% and in particular for CPU systems, SkePU can outperform hand-written code.
December 11, 2022 by hgpu