Abstract
A simple model of noise with an adjustable level of asynchrony is presented. The model is used to generate synthetic noise traces in the presence of a representative bulk synchronous, nearest neighbor time stepping algorithm. The resulting performance of the algorithm is measured and compared to the performance of the algorithm in the presence of Gaussian distributed noise. The results empirically illustrate that asynchrony is a dominant mechanism by which many types of computational noise degrade the performance of bulk-synchronous algorithms, whether or not their macroscopic noise distributions are constant or random.
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Acknowledgements
This research used resources of the Argonne Leadership Computing Facility at Argonne National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under contract DE-AC02-06CH11357.
This research used the University of Delaware’s Chimera computer, funded by U.S. National Science Foundation award CNS-0958512. S.F. Siegel was supported by NSF award CCF-0953210.
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Hammouda, A., Siegel, A., Siegel, S. (2015). Overcoming Asynchrony: An Analysis of the Effects of Asynchronous Noise on Nearest Neighbor Synchronizations. In: Markidis, S., Laure, E. (eds) Solving Software Challenges for Exascale. EASC 2014. Lecture Notes in Computer Science(), vol 8759. Springer, Cham. https://doi.org/10.1007/978-3-319-15976-8_7
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