A hybrid approach to parallel pattern discovery in C++
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)
ISSN
1066-6192Type
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.Collections
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