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Memory-Access-Pattern Analysis Techniques for OpenCL Kernels

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Languages and Compilers for Parallel Computing (LCPC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11403))

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Abstract

Previous pattern-by-pattern approaches for OpenCL/CUDA memory optimization require explicit user interventions to extract the kernel memory access patterns. This paper presents an automatic memory-access-pattern analysis framework called MAPA. It is based on a source-level analysis technique derived from traditional symbolic analyses and a run-time pattern selection technique. We propose formal notations of the memory access patterns, analysis algorithms based on the SSA form, and the integration method of MAPA with auto-tuners. The experimental results indicate that MAPA properly analyzes 116 real-world OpenCL kernels from Rodinia and Parboil benchmark suites. We also show an auto-tuner case study, Auto-Dymaxion, which exploits MAPA to automate a memory-access-pattern-based optimization approach.

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science and ICT (MSIT) (No. 2013R1A3A2003664), PF Class Heterogeneous High Performance Computer Development through the NRF funded by the MSIT (No. 2016M3C4A7952587), and BK21 Plus for Pioneers in Innovative Computing (Dept. of Computer Science and Engineering, SNU) through the NRF funded by the Ministry of Education (21A20151113068). ICT at Seoul National University provided research facilities for this study.

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Authors and Affiliations

  1. Center for Manycore Programming, Department of Computer Science and Engineering, Seoul National University, Seoul, 08826, Korea

    Gangwon Jo, Jaehoon Jung, Jiyoung Park & Jaejin Lee

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  1. Gangwon Jo
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  2. Jaehoon Jung
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Correspondence to Gangwon Jo .

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  1. Texas A&M University, College Station, TX, USA

    Lawrence Rauchwerger

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Jo, G., Jung, J., Park, J., Lee, J. (2019). Memory-Access-Pattern Analysis Techniques for OpenCL Kernels. In: Rauchwerger, L. (eds) Languages and Compilers for Parallel Computing. LCPC 2017. Lecture Notes in Computer Science(), vol 11403. Springer, Cham. https://doi.org/10.1007/978-3-030-35225-7_9

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  • DOI: https://doi.org/10.1007/978-3-030-35225-7_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-35224-0

  • Online ISBN: 978-3-030-35225-7

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