Scalable load balancing for massively parallel distributed Monte Carlo particle transport

Conference · Mon Jul 01 00:00:00 EDT 2013

OSTI ID:22212745

O'Brien, M. J.; Brantley, P. S. ^[1]; Joy, K. I. ^[2]

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1. Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, CA 94550 (United States)
2. Institute for Data Analysis and Visualization, Computer Science Department, University of California, One Shields Avenue, Davis, CA 95616 (United States)

In order to run computer simulations efficiently on massively parallel computers with hundreds of thousands or millions of processors, care must be taken that the calculation is load balanced across the processors. Examining the workload of every processor leads to an unscalable algorithm, with run time at least as large as O(N), where N is the number of processors. We present a scalable load balancing algorithm, with run time 0(log(N)), that involves iterated processor-pair-wise balancing steps, ultimately leading to a globally balanced workload. We demonstrate scalability of the algorithm up to 2 million processors on the Sequoia supercomputer at Lawrence Livermore National Laboratory. (authors)

• Research Organization: American Nuclear Society, 555 North Kensington Avenue, La Grange Park, IL 60526 (United States)
• OSTI ID: 22212745
• Resource Relation: Conference: M and C 2013: 2013 International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering, Sun Valley, ID (United States), 5-9 May 2013; Other Information: Country of input: France; 13 refs.; Related Information: In: Proceedings of the 2013 International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering - M and C 2013| 3016 p.
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICAL METHODS AND COMPUTING
ALGORITHMS
COMPUTERIZED SIMULATION
LAWRENCE LIVERMORE NATIONAL LABORATORY
MONTE CARLO METHOD
PARALLEL PROCESSING
PERFORMANCE
SUPERCOMPUTERS
TRANSPORT THEORY