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    Inter-loop optimizations in RAJA using loop chains

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    RAJALC.pdf
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    Author
    Neth, B.
    Scogland, T.R.W.
    de Supinski, B.R.
    Strout, M.M.
    Affiliation
    University of Arizona
    Issue Date
    2021-06
    Keywords
    C++
    Data locality
    Loop chains
    Performance portability
    Polyhedral analysis
    RAJA
    Symbolic execution
    
    Metadata
    Show full item record
    Publisher
    Association for Computing Machinery
    Citation
    Neth, B., Scogland, T. R. W., de Supinski, B. R., & Strout, M. M. (2021). Inter-loop optimizations in RAJA using loop chains. Proceedings of the International Conference on Supercomputing, 1–12.
    Journal
    Proceedings of the International Conference on Supercomputing
    Rights
    © 2021 Association for Computing Machinery.
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    Abstract
    Typical parallelization approaches such as OpenMP and CUDA provide constructs for parallelizing and blocking for data locality for individual loops. By focusing on each loop separately, these approaches fail to leverage sources of data locality possible due to inter-loop data reuse. The loop chain abstraction provides a framework for reasoning about and applying inter-loop optimizations. In this work, we incorporate the loop chain abstraction into RAJA, a performance portability layer for high-performance computing applications. Using the loop-chain-extended RAJA, or RAJALC, developers can have the RAJA library apply loop transformations like loop fusion and overlapped tiling while maintaining the original structure of their programs. By introducing targeted symbolic evaluation capabilities, we can collect and cache data access information required to verify loop transformations. We evaluate the performance improvement and refactoring costs of our extension. Overall, our results demonstrate 85-98% of the performance improvements of hand-optimized kernels with dramatically fewer code changes. © 2021 Association for Computing Machinery.
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    Immediate access
    Version
    Final accepted manuscript
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