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Refactoring for introducing and tuning parallelism for heterogeneous multicore machines in Erlang

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Date
24/06/2019
Author
Janjic, Vladimir
Brown, Christopher
Barwell, Adam
Hammond, Kevin
Keywords
Erlang
GPU
Heterogenous
Multi-core
Parallelism
Refactoring
QA75 Electronic computers. Computer science
NDAS
BDC
R2C
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Abstract
This paper presents semi‐automatic software refactorings to introduce and tune structured parallelism in sequential Erlang code, as well as to generate code for running computations on GPUs and possibly other accelerators. Our refactorings are based on the lapedo framework for programming heterogeneous multi‐core systems in Erlang. lapedo is based on the PaRTE refactoring tool and also contains (1) a set of hybrid skeletons that target both CPU and GPU processors, (2) novel refactorings for introducing and tuning parallelism, and (3) a tool to generate the GPU offloading and scheduling code in Erlang, which is used as a component of hybrid skeletons. We demonstrate, on four realistic use‐case applications, that we are able to refactor sequential code and produce heterogeneous parallel versions that can achieve significant and scalable speedups of up to 220 over the original sequential Erlang program on a 24‐core machine with a GPU.
Citation
Janjic , V , Brown , C , Barwell , A & Hammond , K 2019 , ' Refactoring for introducing and tuning parallelism for heterogeneous multicore machines in Erlang ' , Concurrency and Computation : Practice and Experience , vol. Early View , e5420 , pp. 1-25 . https://doi.org/10.1002/cpe.5420
Publication
Concurrency and Computation : Practice and Experience
Status
Peer reviewed
DOI
https://doi.org/10.1002/cpe.5420
ISSN
1532-0634
Type
Journal article
Rights
© 2019, John Wiley & Sons, Ltd. This work has been made available online in accordance with the publisher’s policies. This is the author created accepted version manuscript following peer review and as such may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1002/cpe.5420
Description
This research has been generously supported by the European Union Framework 7 Para-Phrase project (IST-288570), EU Horizon 2020 projects RePhrase (H2020-ICT-2014-1), agreement number 644235; Teamplay (H2020-ICT 2017-1) agreement number 779882, and EPSRC Discovery, EP/P020631/1. EU COST Action IC1202: Timing Analysis On Code-Level (TACLe), and by a travel grant from EU HiPEAC.
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  • University of St Andrews Research
URI
http://hdl.handle.net/10023/20135

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