Paper Info
Title
A Framework to Analyze the Performance of Load Balancing Schemes for Ensembles of Stochastic Simulations
Abstract
Ensembles of simulations are employed to estimate the statistics of possible future states of a system, and are widely used in important applications such as climate change and biological modeling. Ensembles of runs can naturally be executed in parallel. However, when the CPU times of individual simulations vary considerably, a simple strategy of assigning an equal number of tasks per processor can lead to serious work imbalances and low parallel efficiency. This paper presents a new probabilistic framework to analyze the performance of dynamic load balancing algorithms for ensembles of simulations where many tasks are mapped onto each processor, and where the individual compute times vary considerably among tasks. Four load balancing strategies are discussed: most-dividing, all-redistribution, random-polling, and neighbor-redistribution. Simulation results with a stochastic budding yeast cell cycle model are consistent with the theoretical analysis. It is especially significant that there is a provable global decrease in load imbalance for the local rebalancing algorithms due to scalability concerns for the global rebalancing algorithms. The overall simulation time is reduced by up to 25 %, and the total processor idle time by 85 %.
Year
DOI
Venue
2015
10.1007/s10766-014-0309-6
International Journal of Parallel Programming
Keywords
Field
DocType
parallel computation
Central processing unit,Load balancing (computing),Computer science,Parallel computing,Theoretical computer science,Biological modeling,Dynamic load balancing,Technical report,Scalability,Idle time,Probabilistic framework
Journal
Volume
Issue
ISSN
43
4
1573-7640
Citations 
PageRank 
References 
3
0.40
24
Authors
6
Name
Order
Citations
PageRank
Tae-Hyuk Ahn1194.47
Adrian Sandu232558.93
Layne T. Watson31253290.45
Clifford A. Shaffer4999131.98
Yang Cao5335.29
William T Baumann6264.87