research-article

SYCL-BLAS: Leveraging Expression Trees for Linear Algebra

Authors:

José I. Aliaga,

Ruymán Reyes,

Mehdi GoliAuthors Info & Claims

IWOCL '17: Proceedings of the 5th International Workshop on OpenCL

Article No.: 32, Pages 1 - 5

https://doi.org/10.1145/3078155.3078189

Published: 16 May 2017 Publication History

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Abstract

In the current landscape of C++ applications, there is an increasing need of including different levels of support for heterogeneous platforms, where multiple specialised devices collaborate to execute an application. In this context, the SYCL standard[8] has been published by Khronos, providing a C++ abstraction layer on top of OpenCL[9] that enables single-source programming for a large number of heterogeneous devices. SYCL single-source programming and task data-flow approach enable developers to leverage modern programming techniques on heterogeneous platforms. In this paper, we present SYCL-BLAS, a BLAS implementation using SYCL that uses Expression Tree templates to generate BLAS kernels. This technique is then used to demonstrate seamless kernel fusion via composition of tree nodes. We also demonstrate how SYCL can be used to quickly develop libraries for heterogeneous systems by providing sufficient levels of abstraction.

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Homerding BVargas AScogland TChen RDavis MHornung R(2024)Enabling RAJA on Intel GPUs with SYCLProceedings of the 12th International Workshop on OpenCL and SYCL10.1145/3648115.3648131(1-10)Online publication date: 8-Apr-2024
https://dl.acm.org/doi/10.1145/3648115.3648131
Ghiglio PDolinsky UGoli MNarasimhan K(2023)Improving performance of SYCL applications on CPU architectures using LLVM‐directed compilation flowConcurrency and Computation: Practice and Experience10.1002/cpe.781035:27Online publication date: 30-May-2023
https://doi.org/10.1002/cpe.7810
Brunton RLomüller V(2022)Improved address space inference for SYCL programsProceedings of the 10th International Workshop on OpenCL10.1145/3529538.3529998(1-2)Online publication date: 10-May-2022
https://dl.acm.org/doi/10.1145/3529538.3529998
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IWOCL '17: Proceedings of the 5th International Workshop on OpenCL

May 2017

135 pages

ISBN:9781450352147

DOI:10.1145/3078155

General Chairs:
Simon McIntosh-Smith
University of Bristol, UK
,
Ben Bergen
Los Alamos National Laboratory, USA

© 2017 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Published: 16 May 2017

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IWOCL 2017: 5th International Workshop on OpenCL

May 16 - 18, 2017

Toronto, Canada

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IWOCL '17 Paper Acceptance Rate 15 of 29 submissions, 52%;

Overall Acceptance Rate 84 of 152 submissions, 55%

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Homerding BVargas AScogland TChen RDavis MHornung R(2024)Enabling RAJA on Intel GPUs with SYCLProceedings of the 12th International Workshop on OpenCL and SYCL10.1145/3648115.3648131(1-10)Online publication date: 8-Apr-2024
https://dl.acm.org/doi/10.1145/3648115.3648131
Ghiglio PDolinsky UGoli MNarasimhan K(2023)Improving performance of SYCL applications on CPU architectures using LLVM‐directed compilation flowConcurrency and Computation: Practice and Experience10.1002/cpe.781035:27Online publication date: 30-May-2023
https://doi.org/10.1002/cpe.7810
Brunton RLomüller V(2022)Improved address space inference for SYCL programsProceedings of the 10th International Workshop on OpenCL10.1145/3529538.3529998(1-2)Online publication date: 10-May-2022
https://dl.acm.org/doi/10.1145/3529538.3529998
Ghiglio PDolinsky UGoli MNarasimhan K(2022)Improving performance of SYCL applications on CPU architectures using LLVM-directed compilation flowProceedings of the Thirteenth International Workshop on Programming Models and Applications for Multicores and Manycores10.1145/3528425.3529099(1-10)Online publication date: 2-Apr-2022
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Sabino TGoli M(2021)Toward Performance Portability of Highly Parametrizable TRSM Algorithm Using SYCLProceedings of the 9th International Workshop on OpenCL10.1145/3456669.3456694(1-10)Online publication date: 27-Apr-2021
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Lawson JGoli M(2021)Performance portability through machine learning guided kernel selection in SYCL librariesParallel Computing10.1016/j.parco.2021.102813107:COnline publication date: 1-Oct-2021
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Lamzed-Short ALaw TMallinson AMudalige GJarvis S(2020)Towards Automated Kernel Fusion for the Optimisation of Scientific Applications2020 IEEE/ACM 6th Workshop on the LLVM Compiler Infrastructure in HPC (LLVM-HPC) and Workshop on Hierarchical Parallelism for Exascale Computing (HiPar)10.1109/LLVMHPCHiPar51896.2020.00010(45-55)Online publication date: Nov-2020
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Jin ZMorozov VFinkel H(2020)A Case Study on the HACCmk Routine in SYCL on Integrated Graphics2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)10.1109/IPDPSW50202.2020.00071(368-374)Online publication date: May-2020
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Jin ZFinkel H(2019)A Case Study of k-means Clustering using SYCL2019 IEEE International Conference on Big Data (Big Data)10.1109/BigData47090.2019.9005555(4466-4471)Online publication date: Dec-2019
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A Comparison of SYCL, OpenCL, CUDA, and OpenMP for Massively Parallel Support Vector Machine Classification on Multi-Vendor Hardware

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A Comparison of SYCL, OpenCL, CUDA, and OpenMP for Massively Parallel Support Vector Machine Classification on Multi-Vendor Hardware

Exploring the performance and portability of the k-means algorithm on SYCL across CPU and GPU architectures

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