Understanding Portability of Automotive Workload: A Case Study with the Points-to-Image Kernel in SYCL on Heterogeneous Computing Platforms

Zheming Jin; Jeffrey S. Vetter

doi:10.1145/3589236.3589238

Understanding Portability of Automotive Workload: A Case Study with the Points-to-Image Kernel in SYCL on Heterogeneous Computing Platforms

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

Abstract

SYCL is a promising programming model for heterogenous computing across vendors’ devices. In this paper, we study whether SYCL can be applied to an automotive workload and its portability on heterogeneous computing platforms. We explain the automotive benchmark, describe our implementations and optimizations of the benchmark, and evaluate the performance of the benchmarks using SYCL and other programming models on heterogeneous computing devices. The study also allows us to have a better understanding of portability of the benchmark across these platforms.

Original language	English
Title of host publication	Proceedings of the 15th Workshop on General Purpose Processing Using GPU, GPGPU 2023
Publisher	Association for Computing Machinery
Pages	14-20
Number of pages	7
ISBN (Electronic)	9798400707766
DOIs	https://doi.org/10.1145/3589236.3589238
State	Published - Feb 25 2023
Event	15th Annual Workshop on General Purpose Processing using Graphics Processing Unit, GPGPU 2023 - Montreal, Canada Duration: Feb 25 2023 → …

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	15th Annual Workshop on General Purpose Processing using Graphics Processing Unit, GPGPU 2023
Country/Territory	Canada
City	Montreal
Period	02/25/23 → …

Funding

We appreciate the reviewers’ comments and suggestions and acknowledge support from the ACM Publishing System staff. This research used resources of the Experimental Computing Lab at Oak Ridge National Laboratory and the Intel DevCloud. 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’ comments and suggestions and acknowledge support from the ACM Publishing System staff. This research used resources of the Experimental Computing Lab at Oak Ridge National Laboratory and the Intel DevCloud. This research was supported by the US Department of Energy Advanced Scientific Computing Research program under Contract No. DEAC05-00OR22725.

Keywords

FPGA
GPU
SYCL
benchmark
portability

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10.1145/3589236.3589238

Cite this

Jin, Z., & Vetter, J. S. (2023). Understanding Portability of Automotive Workload: A Case Study with the Points-to-Image Kernel in SYCL on Heterogeneous Computing Platforms. In Proceedings of the 15th Workshop on General Purpose Processing Using GPU, GPGPU 2023 (pp. 14-20). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3589236.3589238

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abstract = "SYCL is a promising programming model for heterogenous computing across vendors{\textquoteright} devices. In this paper, we study whether SYCL can be applied to an automotive workload and its portability on heterogeneous computing platforms. We explain the automotive benchmark, describe our implementations and optimizations of the benchmark, and evaluate the performance of the benchmarks using SYCL and other programming models on heterogeneous computing devices. The study also allows us to have a better understanding of portability of the benchmark across these platforms.",

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Jin, Z & Vetter, JS 2023, Understanding Portability of Automotive Workload: A Case Study with the Points-to-Image Kernel in SYCL on Heterogeneous Computing Platforms. in Proceedings of the 15th Workshop on General Purpose Processing Using GPU, GPGPU 2023. ACM International Conference Proceeding Series, Association for Computing Machinery, pp. 14-20, 15th Annual Workshop on General Purpose Processing using Graphics Processing Unit, GPGPU 2023, Montreal, Canada, 02/25/23. https://doi.org/10.1145/3589236.3589238

Understanding Portability of Automotive Workload: A Case Study with the Points-to-Image Kernel in SYCL on Heterogeneous Computing Platforms. / Jin, Zheming; Vetter, Jeffrey S.
Proceedings of the 15th Workshop on General Purpose Processing Using GPU, GPGPU 2023. Association for Computing Machinery, 2023. p. 14-20 (ACM International Conference Proceeding Series).

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

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Jin Z, Vetter JS. Understanding Portability of Automotive Workload: A Case Study with the Points-to-Image Kernel in SYCL on Heterogeneous Computing Platforms. In Proceedings of the 15th Workshop on General Purpose Processing Using GPU, GPGPU 2023. Association for Computing Machinery. 2023. p. 14-20. (ACM International Conference Proceeding Series). doi: 10.1145/3589236.3589238

Understanding Portability of Automotive Workload: A Case Study with the Points-to-Image Kernel in SYCL on Heterogeneous Computing Platforms

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