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Evaluating Performance Portability of GPU Programming Models
DescriptionMaintaining 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.
Event Type
Posters
Research Posters
TimeWednesday, 15 November 202310:30am - 12pm MST
Location505
Heterogeneous Computing
Performance Measurement, Modeling, and Tools
TP