PLuTo: A practical and fully automatic polyhedral program optimization system. We present the design and implementation of a fully automatic polyhedral source-to-source transformation framework that can optimize regular programs (sequences of possibly imperfectly nested loops) for parallelism and locality simultaneously. Through this work, we show the practicality of analytical model-driven automatic transformation in the polyhedral model – far beyond what is possible by current production compilers. Unlike previous works, our approach is an end-to-end fully automatic one driven by an integer linear optimization framework that takes an explicit view of finding good ways of tiling for parallelism and locality using affine transformations. We also address generation of tiled code for multiple statement domains of arbitrary dimensionalities under (statement-wise) affine transformations – an issue that has not been addressed previously. Experimental results from the implemented system show very high speedups for local and parallel execution on multi-cores over state-of-the-art compiler frameworks from the research community as well as the best native compilers. The system also enables the easy use of powerful empirical/iterative optimization for general arbitrarily nested loop sequences.
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References in zbMATH (referenced in 2 articles )
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- Nelson, Thomas; Belter, Geoffrey; Siek, Jeremy G.; Jessup, Elizabeth; Norris, Boyana: Reliable generation of high-performance matrix algebra (2015)
- Narayanan, Sri Hari Krishna; Norris, Boyana; Hovland, Paul; Gebremedhin, Assefaw: Implementation of partial separability in a source-to-source transformation AD tool (2012)