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Refactoring GrPPI : generic refactoring for generic parallelism in C++

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Brown2020_Article_RefactoringGrPPIGenericRefacto.pdf (476.6Kb)
Date
10/07/2020
Author
Brown, Christopher Mark
Janjic, Vladimir
Barwell, Adam David
Garcia, Jose Daniel
MacKenzie, K.
Funder
European Commission
EPSRC
Grant ID
779882
EP/P020631/1
Keywords
Refactoring
Parallelism
Parallel patterns
TBB
C++
GrPI
C++ threads
QA75 Electronic computers. Computer science
T-NDAS
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Abstract
The Generic Reusable Parallel Pattern Interface (GrPPI) is a very useful abstraction over different parallel pattern libraries, allowing the programmer to write generic patterned parallel code that can easily be compiled to different backends such as FastFlow, OpenMP, Intel TBB and C++ threads. However, rewriting legacy code to use GrPPI still involves code transformations that can be highly non-trivial, especially for programmers who are not experts in parallelism. This paper describes software refactorings to semi-automatically introduce instances of GrPPI patterns into sequential C++ code, as well as safety checking static analysis mechanisms which verify that introducing patterns into the code does not introduce concurrency-related bugs such as race conditions. We demonstrate the refactorings and safety-checking mechanisms on four simple benchmark applications, showing that we are able to obtain, with little effort, GrPPI-based parallel versions that accomplish good speedups (comparable to those of manually-produced parallel versions) using different pattern backends.
Citation
Brown , C M , Janjic , V , Barwell , A D , Garcia , J D & MacKenzie , K 2020 , ' Refactoring GrPPI : generic refactoring for generic parallelism in C++ ' , International Journal of Parallel Programming , vol. First Online . https://doi.org/10.1007/s10766-020-00667-x
Publication
International Journal of Parallel Programming
Status
Peer reviewed
DOI
https://doi.org/10.1007/s10766-020-00667-x
ISSN
0885-7458
Type
Journal article
Rights
Copyright © 2020 the Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Description
Funding: EU Horizon 2020 project, TeamPlay (https://www.teamplay-xh2020.eu), Grant Number 779882, UK EPSRC Discovery, grant number EP/P020631/1, and Madrid Regional Government, CABAHLA-CM (ConvergenciA Big dAta-Hpc: de Los sensores a las Aplicaciones) Grant Number S2018/TCS-4423.
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  • University of St Andrews Research
URI
http://hdl.handle.net/10023/20321

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