Abstract
Scientific computing applications with highly demanding data capacity and computation power drive a computing platform migration from shared memory machines to multi-core/multiprocessor computer clusters. However, overheads in coordinating operations across computing nodes could counteract the benefit of having extra machines. Furthermore, the hidden dependency in applications slows down the simulation over non-shared memory machines. This paper proposed a framework to utilize multi-core/multiprocessor clusters for distributed simulation. Among several coordination schemes, decentralized control approach has demonstrated its effectiveness in reducing the communication overheads. A speculative execution strategy is applied to exploit parallelism thoroughly and overcome strong data dependency. Performance analysis and experiments are provided to demonstrate the performance gains.
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Li, R., Jiang, H., Su, HC., Zhang, B., Jenness, J. (2008). Parallel and Distributed Particle Collision Simulation with Decentralized Control. In: Wu, S., Yang, L.T., Xu, T.L. (eds) Advances in Grid and Pervasive Computing. GPC 2008. Lecture Notes in Computer Science, vol 5036. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68083-3_25
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DOI: https://doi.org/10.1007/978-3-540-68083-3_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68081-9
Online ISBN: 978-3-540-68083-3
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