TY - GEN
T1 - Using Load Balancing to Scalably Parallelize Sampling-Based Motion Planning Algorithms
AU - Fidel, Adam
AU - Jacobs, Sam Ade
AU - Sharma, Shishir
AU - Amato, Nancy M.
AU - Rauchwerger, Lawrence
N1 - KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUS-C1-016-04
Acknowledgements: This research supported in part by NSF awards CNS-0551685, CCF-0833199, CCF-0830753, IIS-0916053, IIS-0917266, EFRI-1240483, RI-1217991, by NIH NCI R25 CA090301-11, by DOE awards DE-AC02-06CH11357, B575363, by Samsung, Chevron, IBM, Intel, Oracle/Sun andby Award KUS-C1-016-04, made by King Abdullah University of Scienceand Technology (KAUST). This research used resources of the NationalEnergy Research Scientific Computing Center, which is supported by theOffice of Science of the U.S. Department of Energy under Contract No.DE-AC02-05CH11231.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.
PY - 2014/5
Y1 - 2014/5
N2 - 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. © 2014 IEEE.
AB - 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. © 2014 IEEE.
UR - http://hdl.handle.net/10754/600151
UR - http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6877290
UR - http://www.scopus.com/inward/record.url?scp=84906705257&partnerID=8YFLogxK
U2 - 10.1109/IPDPS.2014.66
DO - 10.1109/IPDPS.2014.66
M3 - Conference contribution
SN - 9781479938001
SP - 573
EP - 582
BT - 2014 IEEE 28th International Parallel and Distributed Processing Symposium
PB - Institute of Electrical and Electronics Engineers (IEEE)
ER -