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Putting Automatic Polyhedral Compilation for GPGPU to Work

Soufiane Baghdadi 1 Armin Größlinger 1, 2 Albert Cohen 1
1 ALCHEMY - Architectures, Languages and Compilers to Harness the End of Moore Years
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
Abstract : Automatic parallelization is becoming more important as parallelism becomes ubiquitous. The first step for achieving automation is to develop a theoretical foundation, for example, the polyhedron model. The second step is to implement the algorithms studied in the theoretical framework and getting them to work in a compiler that can be used to parallelize real codes. The polyhedral model is a well-established theoretical foundation for parallelizing codes with static control. In this paper, we present, from a practical point of view, the challenges to solve for getting polyhedral compilation for GPUs to work. We choose the Polyhedral Compiler Collection (PoCC) as compiler infrastructure and target CUDA as the target platform; we plan to support OpenCL in the future.
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https://hal.inria.fr/inria-00551517
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Submitted on : Tuesday, January 4, 2011 - 12:44:41 AM
Last modification on : Tuesday, June 15, 2021 - 4:10:40 PM
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Soufiane Baghdadi, Armin Größlinger, Albert Cohen. Putting Automatic Polyhedral Compilation for GPGPU to Work. Proceedings of the 15th Workshop on Compilers for Parallel Computers (CPC'10), Jul 2010, Vienna, Austria. ⟨inria-00551517⟩

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