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Performance modeling of hyper-scale custom machine for the principal steps in block Wiedemann algorithm

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Abstract

Solving large-scale sparse linear systems over GF(2) plays a key role in fluid mechanics, simulation and design of materials, petroleum seismic data processing, numerical weather prediction, computational electromagnetics, and numerical simulation of unclear explosions. Therefore, developing algorithms for this issue is a significant research topic. In this paper, we proposed a hyper-scale custom supercomputer architecture that matches specific data features to process the key procedure of block Wiedemann algorithm and its parallel algorithm on the custom machine. To increase the computation, communication, and storage performance, four optimization strategies are proposed. This paper builds a performance model to evaluate the execution performance and power consumption for our custom machine. The model shows that the optimization strategies result in a considerable speedup, even three times faster than the fastest supercomputer, TH2, while consuming less power.

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Acknowledgments

This work was funded by National Natural Science Foundation of China (number 61303070). We acknowledge TH-1A supercomputing system service to support our simulation. We would like to thank the reviewers for their helpful comments.

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  1. National University of Defense Technology, Changsha, China

    Tong Zhou & Jingfei Jiang

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  1. Tong Zhou
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  2. Jingfei Jiang
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Correspondence to Jingfei Jiang.

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Zhou, T., Jiang, J. Performance modeling of hyper-scale custom machine for the principal steps in block Wiedemann algorithm. J Supercomput 72, 4181–4203 (2016). https://doi.org/10.1007/s11227-016-1767-y

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