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PolyJIT: Polyhedral Optimization Just in Time

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Abstract

While polyhedral optimization appeared in mainstream compilers during the past decade, its profitability in scenarios outside its classic domain of linear-algebra programs has remained in question. Recent implementations, such as the LLVM plugin Polly, produce promising speedups, but the restriction to affine loop programs with control flow known at compile time continues to be a limiting factor. PolyJIT combines polyhedral optimization with multi-versioning at run time, at which one has access to knowledge enabling polyhedral optimization, which is not available at compile time. By means of a fully-fledged implementation of a light-weight just-in-time compiler and a series of experiments on a selection of real-world and benchmark programs, we demonstrate that the consideration of run-time knowledge helps in tackling compile-time violations of affinity and, consequently, offers new opportunities of optimization at run time.

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Notes

  1. Geometrically, these objects are (\(\mathbb {Z}\)-)polyhedra.

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Acknowledgements

All four authors received finanical support by the Deutsche Forschungsgemeinschaft (DFG). The respective projects are PolyJIT (LE 912/14), SafeSPL (AP 206/4) and SafeSPL++ (AP 206/6).

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  1. University of Passau, Passau, Germany

    Andreas Simbürger, Sven Apel, Armin Größlinger & Christian Lengauer

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  1. Andreas Simbürger
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  2. Sven Apel
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  3. Armin Größlinger
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  4. Christian Lengauer
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Correspondence to Andreas Simbürger.

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Simbürger, A., Apel, S., Größlinger, A. et al. PolyJIT: Polyhedral Optimization Just in Time. Int J Parallel Prog 47, 874–906 (2019). https://doi.org/10.1007/s10766-018-0597-3

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