稠密线性代数中基于容错的实用算法研究

Panruo Wu, Qiang Guan, Nathan Debardeleben, S. Blanchard, Dingwen Tao, Xin Liang, Jieyang Chen, Zizhong Chen
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引用次数: 33

摘要

基于算法的容错(ABFT)以其极低的开销和良好的可扩展性重新引起人们的关注。然而,用于设计ABFT的故障模型要么抽象,要么过于简单,要么两者兼而有之,在体系结构级别发生的情况与算法期望的情况之间留下了差距。由于故障模型是选择有效校验和方案的决定性因素,因此所得到的ABFT技术在实践中影响有限。在本文中,我们试图通过直接使用一个全面的架构故障模型来缩小差距,并设计了一个全面的ABFT方案,该方案可以容忍多种不同类型的架构故障。我们在高性能linpack (HPL)中实现了新的ABFT方案,并在大规模高性能基准测试中验证了该方案的可行性。我们分别进行了架构错误注入实验和大规模实验,以经验验证其容错性和演示错误处理的开销。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Towards Practical Algorithm Based Fault Tolerance in Dense Linear Algebra
Algorithm based fault tolerance (ABFT) attracts renewed interest for its extremely low overhead and good scalability. However the fault model used to design ABFT has been either abstract, simplistic, or both, leaving a gap between what occurs at the architecture level and what the algorithm expects. As the fault model is the deciding factor in choosing an effective checksum scheme, the resulting ABFT techniques have seen limited impact in practice. In this paper we seek to close the gap by directly using a comprehensive architectural fault model and devise a comprehensive ABFT scheme that can tolerate multiple architectural faults of various kinds. We implement the new ABFT scheme into high performance linpack (HPL) to demonstrate the feasibility in large scale high performance benchmark. We conduct architectural fault injection experiments and large scale experiments to empirically validate its fault tolerance and demonstrate the overhead of error handling, respectively.
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