Scalable Parallel Best-First Search for Optimal Sequential Planning

dc.contributor.authorKishimoto, Akihiro
dc.contributor.authorFukunaga, Alex
dc.contributor.authorBotea, Adi
dc.coverage.spatialThessaloniki Greece
dc.date.accessioned2015-12-07T22:41:44Z
dc.date.createdSeptember 19-23 2009
dc.date.issued2009
dc.date.updated2016-02-24T11:13:54Z
dc.description.abstractLarge-scale, parallel clusters composed of commodity processors are increasingly available, enabling the use of vast processing capabilities and distributed RAM to solve hard search problems. We investigate parallel algorithms for optimal sequential planning, with an emphasis on exploiting distributed memory computing clusters. In particular, we focus on an approach which distributes and schedules work among processors based on a hash function of the search state. We use this approach to parallelize the A* algorithm in the optimal sequential version of the Fast Downward planner. The scaling behavior of the algorithm is evaluated experimentally on clusters using up to 128 processors, a significant increase compared to previous work in parallelizing planners. We show that this approach scales well, allowing us to effectively utilize the large amount of distributed memory to optimally solve problems which require hundreds of gigabytes of RAM to solve. We also show that this approach scales well for a single, shared-memory multicore machine.
dc.identifier.isbn9781577354062
dc.identifier.urihttp://hdl.handle.net/1885/24451
dc.publisherAAAI Press
dc.relation.ispartofseriesInternational conference on Automated planning and scheduling (ICAPS 2009)
dc.sourceProceedings of the Nineteenth International Conference on Automated Planning and Scheduling
dc.subjectKeywords: Best first search; Commodity processors; Computing clusters; Distributed Memory; Multi-core machines; Optimal sequential; Parallel clusters; Parallelizing; Processing capability; Scaling behavior; Search problem; Shared memories; Hash functions; Optimizat
dc.titleScalable Parallel Best-First Search for Optimal Sequential Planning
dc.typeConference paper
local.contributor.affiliationKishimoto, Akihiro, Tokyo Institute of Technology
local.contributor.affiliationFukunaga, Alex, Tokyo Institute of Technology
local.contributor.affiliationBotea, Adi , College of Engineering and Computer Science, ANU
local.contributor.authoremailrepository.admin@anu.edu.au
local.contributor.authoruidBotea, Adi , u1814829
local.description.embargo2037-12-31
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.absfor080199 - Artificial Intelligence and Image Processing not elsewhere classified
local.identifier.absfor080105 - Expert Systems
local.identifier.ariespublicationu4607519xPUB32
local.identifier.scopusID2-s2.0-78650608544
local.identifier.uidSubmittedByu4607519
local.type.statusPublished Version

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