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A Similarity Measure for GPU Kernel Subgraph Matching

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Languages and Compilers for Parallel Computing (LCPC 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11882))

Abstract

Accelerator architectures specialize in executing SIMD (single instruction, multiple data) in lockstep. Because the majority of CUDA applications are parallelized loops, control flow information can provide an in-depth characterization of a kernel. CUDAflow is a tool that statically separates CUDA binaries into basic block regions and dynamically measures instruction and basic block frequencies. CUDAflow captures this information in a control flow graph (CFG) and performs subgraph matching across various kernel’s CFGs to gain insights into an application’s resource requirements, based on the shape and traversal of the graph, instruction operations executed and registers allocated, among other information. The utility of CUDAflow is demonstrated with SHOC and Rodinia application case studies on a variety of GPU architectures, revealing novel control flow characteristics that facilitate end users, autotuners, and compilers in generating high performing code.

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Authors and Affiliations

  1. University of Oregon, Eugene, OR, USA

    Robert Lim, Boyana Norris & Allen Malony

Authors
  1. Robert Lim
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  2. Boyana Norris
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  3. Allen Malony
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Corresponding author

Correspondence to Robert Lim .

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Editors and Affiliations

  1. University of Utah, Salt Lake City, UT, USA

    Mary Hall

  2. University of Utah, Salt Lake City, UT, USA

    Hari Sundar

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Lim, R., Norris, B., Malony, A. (2019). A Similarity Measure for GPU Kernel Subgraph Matching. In: Hall, M., Sundar, H. (eds) Languages and Compilers for Parallel Computing. LCPC 2018. Lecture Notes in Computer Science(), vol 11882. Springer, Cham. https://doi.org/10.1007/978-3-030-34627-0_3

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  • DOI: https://doi.org/10.1007/978-3-030-34627-0_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-34626-3

  • Online ISBN: 978-3-030-34627-0

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