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A hybrid approach to parallel pattern discovery in C++

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ShortPDP2020.pdf (186.5Kb)
Date
14/05/2020
Author
Brown, Christopher Mark
Janjic, Vladimir
Barwell, Adam David
Thomson, John Donald
Castañeda Lozano, Roberto
Cole, Murray
Franke, Bjoern
Garcia-Sanchez, Jose Daniel
Del Rio Astorga, David
MacKenzie, K.
Keywords
QA75 Electronic computers. Computer science
T Technology
T-NDAS
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Abstract
Parallel pattern libraries offer a strong combination of abstraction and performance. However, discovering places in sequential code where parallel patterns should be introduced is still highly non-trivial, often requiring expert manual analysis and profiling. We present a hybrid discovery technique to detect instances of parallel patterns in sequential code. This employs both static and dynamic trace-based analysis, together with hotspot detection. We evaluate our pattern discovery mechanism on a number of representative benchmarks. We evaluate the performance of the resulting parallelised benchmarks on a 24-core parallel machine.
Citation
Brown , C M , Janjic , V , Barwell , A D , Thomson , J D , Castañeda Lozano , R , Cole , M , Franke , B , Garcia-Sanchez , J D , Del Rio Astorga , D & MacKenzie , K 2020 , A hybrid approach to parallel pattern discovery in C++ . in 2020 28th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP) . , 9092377 , Proceedings - Euromicro Workshop on Parallel and Distributed Processing , IEEE Computer Society , 28th Euromicro International Conference on Parallel, Distributed and Network-based Processing , Västerås , Sweden , 11/03/20 . https://doi.org/10.1109/PDP50117.2020.00035
 
conference
 
Publication
2020 28th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)
DOI
https://doi.org/10.1109/PDP50117.2020.00035
ISSN
1066-6192
Type
Conference item
Rights
Copyright © 2020 IEEE. This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the author created accepted manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1109/PDP50117.2020.00035
Description
Funding: EU Horizon 2020 project, TeamPlay, grant number 779882, and UK EPSRC Discovery, grant number EP/P020631/1.
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  • University of St Andrews Research
URI
http://hdl.handle.net/10023/19088

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