Work

Combining dataflow programming and polyhedral optimization, a case study

Year: 2017

Type: preprint

Abstract: Nowadays, parallel computers have become ubiquitous and currentprocessors contain several execution cores, anywhere from a couple tohundreds. This multi-core tendency is due to constraints preventingthe increase of clock frequencies, such as heat generation and powerconsumption. A variety of low-level tools exist to program these chipsefficiently, but they are considered hard to program, to maintain, andto debug, because they may exhibit non-deterministic behaviors. Thisproject focuses on adding an abstraction level in order to have asmuch performance as possible while not dealing with low-levelmechanisms. The approach is based on data flow programming, whichallows the programmer to specify only the operations to perform andtheir dependencies, without actually scheduling them. This projectcombines this paradigm with the Polyhedral Model, which allowsautomatic parallelization and optimization of loop nests, in order tomake the programming easier by delegating work to the compilers andstatic analyzers.

Authors: Romain Fontaine, Lionel Morel, Laure Gonnord

Institutions: Institut National des Sciences Appliquées de Lyon, Laboratoire de l'Informatique du Parallélisme, ROMA: Optimisation des ressources : modèles, algorithmes et ordonnancement

Language: English

Topic: Parallel Computing and Optimization Techniques

Subfield: Hardware and Architecture

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2745259217

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