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Evaluating the Performance and Conformance of a SYCL Implementation for SX-Aurora TSUBASA

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Parallel and Distributed Computing, Applications and Technologies (PDCAT 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 13148))

Abstract

SX-Aurora TSUBASA (SX-AT) is a vector supercomputer equipped with Vector Engines (VEs). SX-AT has not only such a new system architecture, but also some execution modes to achieve high performance on executing a real-world application that often consists of vector friendly and unfriendly parts. Vector Engine Offloading (VEO) is a programming framework to offload only a vector-friendly part to VEs, and neoSYCL has been developed on top of VEO to allow programmers to use the standard SYCL interface at offload programming on SX-AT. However, it is unclear how much neoSYCL based on VEO can conform to the SYCL standard, which is primarily based on OpenCL. Therefore, this paper discusses the conformance of neoSYCL to the SYCL standard, and also the performance. Our thorough evaluation with SYCL-Bench kernels demonstrates that neoSYCL is conformant to the SYCL standard except for OpenCL-related features. In addition, the runtime overhead for using the SYCL interface on top of VEO is negligible in most cases, allowing the neoSYCL codes to achieve comparable performance with the VEO codes.

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Notes

  1. 1.

    https://github.com/Tohoku-University-Takizawa-Lab/veo-sycl-bench.

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Acknowledgement

This work is partially supported by MEXT Next Generation High-Performance Computing Infrastructures and Applications R&D Program “R&D of A Quantum-Annealing-Assisted Next Generation HPC Infrastructure and its Applications,” Grant-in-Aid for Scientific Research(A) #20H00593 and Grant-in-Aid for Scientific Research(B) #21H03449.

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Correspondence to Jiahao Li .

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Li, J., Agung, M., Takizawa, H. (2022). Evaluating the Performance and Conformance of a SYCL Implementation for SX-Aurora TSUBASA. In: Shen, H., et al. Parallel and Distributed Computing, Applications and Technologies. PDCAT 2021. Lecture Notes in Computer Science(), vol 13148. Springer, Cham. https://doi.org/10.1007/978-3-030-96772-7_4

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  • DOI: https://doi.org/10.1007/978-3-030-96772-7_4

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