Understanding Performance Portability of SYCL Kernels: A Case Study with the All-Pairs Distance Calculation in Bioinformatics on GPUs

Zheming Jin; Jeffrey S. Vetter

doi:10.1109/IPDPSW59300.2023.00067

Understanding Performance Portability of SYCL Kernels: A Case Study with the All-Pairs Distance Calculation in Bioinformatics on GPUs

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

SYCL is a portable programming model. Toward the goal of a better understanding of performance portability of SYCL kernels on GPUs, we select a bioinformatics kernel for computing the all-pairs distance as a case study. After migrating the kernel from CUDA to HIP and SYCL, we evaluate the performance of the CUDA, HIP, and SYCL kernels on NVIDIA V100 and AMD MI210 GPUs. We analyze the GPU instructions from the kernels to explain performance gaps between SYCL and CUDA/HIP. We hope that the findings are valuable for improving performance portability of SYCL.

Original language	English
Title of host publication	2023 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	366-372
Number of pages	7
ISBN (Electronic)	9798350311990
DOIs	https://doi.org/10.1109/IPDPSW59300.2023.00067
State	Published - 2023
Event	2023 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2023 - St. Petersburg, United States Duration: May 15 2023 → May 19 2023

Publication series

Name	2023 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2023

Conference

Conference	2023 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2023
Country/Territory	United States
City	St. Petersburg
Period	05/15/23 → 05/19/23

Funding

We appreciate the reviewers for their comments and suggestions. The research used resources at the Experimental Computing Lab and the Compute and Data Environment for Science at Oak Ridge National Laboratory. This research was supported by the US Department of Energy Advanced Scientific Computing Research program under Contract No. DE-AC05-00OR22725. We appreciate the reviewers for their comments and suggestions. The research used resources at the Experimental Computing Lab and the Compute and Data Environment for Science at Oak Ridge National Laboratory. This research was supported by the US Department of Energy Advanced Scientific Computing Research program under Contract No. DE-AC05- 00OR22725.

Funders	Funder number
US Department of Energy Advanced Scientific Computing Research	DE-AC05-00OR22725
Oak Ridge National Laboratory

Keywords

Heterogeneous computing
Performance portabuity
programming model

Access to Document

10.1109/IPDPSW59300.2023.00067

Cite this

Jin, Z., & Vetter, J. S. (2023). Understanding Performance Portability of SYCL Kernels: A Case Study with the All-Pairs Distance Calculation in Bioinformatics on GPUs. In 2023 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2023 (pp. 366-372). (2023 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IPDPSW59300.2023.00067

Jin, Zheming ; Vetter, Jeffrey S. / Understanding Performance Portability of SYCL Kernels : A Case Study with the All-Pairs Distance Calculation in Bioinformatics on GPUs. 2023 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2023. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 366-372 (2023 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2023).

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abstract = "SYCL is a portable programming model. Toward the goal of a better understanding of performance portability of SYCL kernels on GPUs, we select a bioinformatics kernel for computing the all-pairs distance as a case study. After migrating the kernel from CUDA to HIP and SYCL, we evaluate the performance of the CUDA, HIP, and SYCL kernels on NVIDIA V100 and AMD MI210 GPUs. We analyze the GPU instructions from the kernels to explain performance gaps between SYCL and CUDA/HIP. We hope that the findings are valuable for improving performance portability of SYCL.",

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publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Jin, Z & Vetter, JS 2023, Understanding Performance Portability of SYCL Kernels: A Case Study with the All-Pairs Distance Calculation in Bioinformatics on GPUs. in 2023 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2023. 2023 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2023, Institute of Electrical and Electronics Engineers Inc., pp. 366-372, 2023 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2023, St. Petersburg, United States, 05/15/23. https://doi.org/10.1109/IPDPSW59300.2023.00067

Understanding Performance Portability of SYCL Kernels: A Case Study with the All-Pairs Distance Calculation in Bioinformatics on GPUs. / Jin, Zheming; Vetter, Jeffrey S.
2023 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 366-372 (2023 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Jin Z, Vetter JS. Understanding Performance Portability of SYCL Kernels: A Case Study with the All-Pairs Distance Calculation in Bioinformatics on GPUs. In 2023 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 366-372. (2023 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2023). doi: 10.1109/IPDPSW59300.2023.00067

Understanding Performance Portability of SYCL Kernels: A Case Study with the All-Pairs Distance Calculation in Bioinformatics on GPUs

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Keywords

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