DocumentCode :
1783279
Title :
Using Load Balancing to Scalably Parallelize Sampling-Based Motion Planning Algorithms
Author :
Fidel, Adam ; Jacobs, Sam Ade ; Sharma, Shantanu ; Amato, Nancy M. ; Rauchwerger, L.
Author_Institution :
Dept. of Comput. Sci. & Eng., Texas A&M Univ., College Station, TX, USA
fYear :
2014
fDate :
19-23 May 2014
Firstpage :
573
Lastpage :
582
Abstract :
Motion planning, which is the problem of computing feasible paths in an environment for a movable object, has applications in many domains ranging from robotics, to intelligent CAD, to protein folding. The best methods for solving this PSPACE-hard problem are so-called sampling-based planners. Recent work introduced uniform spatial subdivision techniques for parallelizing sampling-based motion planning algorithms that scaled well. However, such methods are prone to load imbalance, as planning time depends on region characteristics and, for most problems, the heterogeneity of the sub problems increases as the number of processors increases. In this work, we introduce two techniques to address load imbalance in the parallelization of sampling-based motion planning algorithms: an adaptive work stealing approach and bulk-synchronous redistribution. We show that applying these techniques to representatives of the two major classes of parallel sampling-based motion planning algorithms, probabilistic roadmaps and rapidly-exploring random trees, results in a more scalable and load-balanced computation on more than 3,000 cores.
Keywords :
parallel algorithms; path planning; resource allocation; sampling methods; PSPACE-hard problem; adaptive work stealing approach; bulk-synchronous redistribution; intelligent CAD; load balancing; load imbalance; movable object; probabilistic roadmaps; protein folding; rapidly-exploring random trees; robotics; scalably parallelize sampling-based motion planning algorithms; uniform spatial subdivision techniques; Joining processes; Load management; Measurement; Planning; Probabilistic logic; Program processors; Proteins;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Parallel and Distributed Processing Symposium, 2014 IEEE 28th International
Conference_Location :
Phoenix, AZ
ISSN :
1530-2075
Print_ISBN :
978-1-4799-3799-8
Type :
conf
DOI :
10.1109/IPDPS.2014.66
Filename :
6877290
Link To Document :
بازگشت