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Runtime Multi-versioning and Specialization inside a Memoized Speculative Loop Optimizer

Abstract : In this paper, we propose a runtime framework that implements code multi-versioning and specialization to optimize and parallelize loop kernels that are invoked many times with varying parameters. These parameters may influence the code structure, the touched memory locations, the work-load, and the runtime performance. They may also impact the validity of the parallelizing and optimizing polyhedral transformations that are applied on-the-fly. For a target loop kernel and its associated parameters, a different optimizing and parallelizing transformation is evaluated at each invocation, among a finite set of transformations (multi-versioning and specialization). The best performing transformed code version is stored and indexed using its associated parameters. When every optimizing transformation has been evaluated, the best performing code version regarding the current parameters, which has been stored, is relaunched at next invocations (memoization).
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Contributor : Philippe Clauss <>
Submitted on : Tuesday, January 28, 2020 - 9:53:43 AM
Last modification on : Monday, November 16, 2020 - 10:20:08 AM
Long-term archiving on: : Wednesday, April 29, 2020 - 1:03:20 PM


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Raquel Lazcano, Daniel Madroñal, Eduardo Juarez, Philippe Clauss. Runtime Multi-versioning and Specialization inside a Memoized Speculative Loop Optimizer. CC 2020 - 29th International Conference on Compiler Construction, Feb 2020, San Diego, United States. ⟨10.1145/3377555.3377886⟩. ⟨hal-02457425⟩



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