Abstract
Combinatorial search is central to many applications, yet the huge irregular search trees and the need to respect search heuristics make it hard to parallelise. We aim to improve the reuse of intricate parallel search implementations by providing the first general purpose scalable parallel framework for exact combinatorial search, YewPar. We make the following contributions. (1) We present a novel formal model of parallel backtracking search, covering enumeration, decision, and optimisation search. (2) We introduce Lazy Node Generators as a uniform API for search tree generation. (3) We present the design and implementation of 12 widely applicable algorithmic skeletons for tree search on shared and distributed memory architectures. (4) Uniquely in the field we demonstrate how a wide range of parallel search applications can easily be constructed by composing Lazy Node Generators and the search skeletons. (5) We report a systematic performance analysis of all 12 YewPar skeletons on standard instances of 7 search applications, investigating skeleton overheads and scalability up to 255 workers on 17 distributed locations.
Original language | English |
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Title of host publication | PPoPP '20: Proceedings of the 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming |
Publisher | Association for Computing Machinery |
Pages | 292–307 |
Number of pages | 16 |
ISBN (Print) | 9781450368186 |
DOIs | |
Publication status | Published - 25 Feb 2020 |
Event | Principles and Practice of Parallel Programming 2020 - San Diego Mission Bay Resort, San Diego, United States Duration: 22 Feb 2020 → 26 Feb 2020 Conference number: 25 https://ppopp20.sigplan.org/ |
Conference
Conference | Principles and Practice of Parallel Programming 2020 |
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Abbreviated title | PPoPP 2020 |
Country/Territory | United States |
City | San Diego |
Period | 22/02/20 → 26/02/20 |
Internet address |
Keywords
- Algorithmic Skeletons
- Combinatorial Search
- Distributed Memory Parallelism
- HPX
ASJC Scopus subject areas
- Software
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Robert James Stewart
- School of Mathematical & Computer Sciences - Associate Professor
- School of Mathematical & Computer Sciences, Computer Science - Associate Professor
Person: Academic (Research & Teaching)