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

Conference · Wed Feb 01 00:00:00 EST 2023

DOI:https://doi.org/10.1145/3589236.3589238· OSTI ID:1996701

Jin, Zheming ^[1]; Vetter, Jeffrey  ^[1]

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1. ORNL

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.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE Office of Science (SC)
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 1996701
• Resource Relation: Conference: Workshop on General Purpose Processing Using GPU (GPGPU) - Montreal, , Canada - 2/25/2023 10:00:00 AM-2/25/2023 10:00:00 AM
• Country of Publication: United States
• Language: English