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Conference Papers Year : 2017

APOLLO: Automatic speculative POLyhedral Loop Optimizer

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Abstract

A few weeks ago, we were glad to announce the first release of Apollo, the Automatic speculative POLyhedral Loop Opti-mizer. Apollo applies polyhedral optimizations on-the-fly to loop nests, whose control flow and memory access patterns cannot be determined at compile-time. In contrast to existing tools, Apollo can handle any kind of loop nest, whose memory accesses can be performed through pointers and in-directions. At runtime, Apollo builds a predictive polyhedral model, which is used for speculative optimization including parallelization. Being a dynamic system, Apollo can even apply the polyhedral model to nonlinear loops. This paper describes Apollo from the perspective of a user, as well as some of its main contributions and mechanisms, including the just-in-time polyhedral compilation, that significantly extends the scope of polyhedral techniques.
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Dates and versions

hal-01533692 , version 1 (06-06-2017)

Identifiers

  • HAL Id : hal-01533692 , version 1

Cite

Juan Manuel Martinez Caamaño, Aravind Sukumaran-Rajam, Artiom Baloian, Manuel Selva, Philippe Clauss. APOLLO: Automatic speculative POLyhedral Loop Optimizer. IMPACT 2017 - 7th International Workshop on Polyhedral Compilation Techniques, Jan 2017, Stockholm, Sweden. pp.8. ⟨hal-01533692⟩
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