Selective Protection for Sparse Iterative Solvers to Reduce the Resilience Overhead
Selective Protection for Sparse Iterative Solvers to Reduce the Resilience Overhead
复制标题
选择性保护稀疏迭代求解器以减少弹性开销
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
P. Raghavan
中科院分区：
文献类型：
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作者：
Hongyang Sun;Ana Gainaru;Manu Shantharam;P. Raghavan
当今高性能计算（HPC）系统的规模和复杂性不断增加，需要重新关注如何增强长期运行的科学应用程序在出现故障时的恢复能力。许多这些应用程序是迭代的性质，因为它们涉及模拟偏微分方程（PDE）的离散空间域上的物理特性的稀疏矩阵上操作。虽然这些应用程序目前受益于系统级别的许多与应用程序无关的弹性技术，如检查点和复制，但部署这些技术的开销很大。在本文中，我们寻求开发应用程序感知的弹性技术，利用迭代应用程序的固有弹性故障和选择性地保护某些元素，从而减少弹性开销。具体来说，我们调查软错误的影响，广泛使用的预处理共轭梯度（PCG）方法，其可靠性在很大程度上取决于通过稀疏矩阵向量乘法（SpMV）操作的错误传播。通过表征与底层稀疏矩阵的数值属性相关的PCG的性能，我们提出了一种选择性保护方案，该方案基于分析模型仅保护操作的某些关键元素。使用SuiteSparse Matrix Collection中的20个稀疏矩阵进行的实验评估表明，与具有完全保护或零保护的基线技术相比，我们提出的方案能够将弹性开销减少多达70.2%，平均减少32.6%。
The increasing scale and complexity of today's high-performance computing (HPC) systems demand a renewed focus on enhancing the resilience of long-running scientific applications in the presence of faults. Many of these applications are iterative in nature as they operate on sparse matrices that concern the simulation of partial differential equations (PDEs) which numerically capture the physical properties on discretized spatial domains. While these applications currently benefit from many application-agnostic resilience techniques at the system level, such as checkpointing and replication, there is significant overhead in deploying these techniques. In this paper, we seek to develop application-aware resilience techniques that leverage an iterative application's intrinsic resiliency to faults and selectively protect certain elements, thereby reducing the resilience overhead. Specifically, we investigate the impact of soft errors on the widely used Preconditioned Conjugate Gradient (PCG) method, whose reliability depends heavily on the error propagation through the sparse matrix-vector multiplication (SpMV) operation. By characterizing the performance of PCG in correlation with a numerical property of the underlying sparse matrix, we propose a selective protection scheme that protects only certain critical elements of the operation based on an analytical model. An experimental evaluation using 20 sparse matrices from the SuiteSparse Matrix Collection shows that our proposed scheme is able to reduce the resilience overhead by as much as 70.2% and an average of 32.6% compared to the baseline techniques with full-protection or zero-protection.