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Adaptive Implementation Selection in the SkePU Skeleton Programming Library
Linköping University, Department of Computer and Information Science, Software and Systems. (PELAB)
Linköping University, Department of Computer and Information Science, Software and Systems. (PELAB)
Linköping University, Department of Computer and Information Science, Software and Systems. (PELAB)ORCID iD: 0000-0001-5241-0026
2013 (English)In: Advanced Parallel Processing Technologies (APPT-2013), Proceedings / [ed] Chengyung Wu and Albert Cohen (eds.), 2013, 170-183 p.Conference paper, Published paper (Refereed)
Abstract [en]

In earlier work, we have developed the SkePU skeleton programming library for modern multicore systems equipped with one or more programmable GPUs. The library internally provides four types of implementations (implementation variants) for each skeleton: serial C++, OpenMP, CUDA and OpenCL targeting either CPU or GPU execution respectively. Deciding which implementation would run faster for a given skeleton call depends upon the computation, problem size(s), system architecture and data locality.

In this paper, we present our work on automatic selection between these implementation variants by an offline machine learning method which generates a compact decision tree with low training overhead. The proposed selection mechanism is flexible yet high-level allowing a skeleton programmer to control different training choices at a higher abstraction level. We have evaluated our optimization strategy with 9 applications/kernels ported to our skeleton library and achieve on average more than 94% (90%) accuracy with just 0.53% (0.58%) training space exploration on two systems. Moreover, we discuss one application scenario where local optimization considering a single skeleton call can prove sub-optimal, and propose a heuristic for bulk implementation selection considering more than one skeleton call to address such application scenarios.

Place, publisher, year, edition, pages
2013. 170-183 p.
Series
Lecture Notes in Computer Science, ISSN 0302-9743 (print), 1611-3349 (online) ; 8299
Keyword [en]
Skeleton programming, SkePU, adaptivity, autotuning, performance optimization, GPU, multicore, parallel computing
National Category
Computer Science
Identifiers
URN: urn:nbn:se:liu:diva-102579DOI: 10.1007/978-3-642-45293-2_13ISBN: 978-3-642-45292-5 (print)OAI: oai:DiVA.org:liu-102579DiVA: diva2:679361
Conference
Advanced Parallel Processing Technologies, Stockholm, August 2013
Projects
EU FP7 PEPPHERSeRC - OpCoReS
Funder
EU, FP7, Seventh Framework Programme, 248481Swedish e‐Science Research Center, OpCoReS
Available from: 2013-12-15 Created: 2013-12-15 Last updated: 2017-02-27

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Dastgeer, UsmanLi, LuKessler, Christoph

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