SC23 Proceedings

The International Conference for High Performance Computing, Networking, Storage, and Analysis

Research Posters Archive

Evaluating Performance Portability of GPU Programming Models

Authors: Joshua H. Davis, Pranav Sivaraman, Isaac Minn, and Abhinav Bhatele (University of Maryland)

Abstract: Maintaining a single codebase that can achieve good performance on a range of accelerator-based supercomputing platforms is of extremely high value for productive scientific application development. However, the large quantity of programming models available which claim to provide performance portability leaves developers with a complex choice when picking a model to use, potentially requiring an intensive effort to test each available model with kernels from their app. In order to better understand the current state of performance portable programming models, this project evaluates seven of the most popular programming models using two memory-bound mini-applications on two leadership-class supercomputers, Summit and Perlmutter. These results provide a useful evaluation of how well each programming model provides true performance portability in real-world usage for memory-bound applications.

Best Poster Finalist (BP): yes

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