Combining Cluster Sampling and ACE analysis to improve fault-injection based reliability evaluation of GPU-based systems
Combining Cluster Sampling and ACE analysis to improve fault-injection based reliability evaluation of GPU-based systems
复制标题
结合聚类采样和 ACE 分析来改进基于 GPU 的系统的基于故障注入的可靠性评估
DOI：
10.1109/dft.2019.8875392
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
S. Carlo
中科院分区：
文献类型：
--
作者：
Alessandro Vallero;S. Carlo
近年来，计算能力需求大幅增长。现代GPU芯片旨在为图形和数据并行通用计算工作负载（GPGPU计算）提供极高的性能。许多GPGPU应用对可靠性有较高的要求，因此可靠性评估成为其设计的关键步骤。评估系统可靠性的最新技术是故障注入和ACE分析。前者尽管时间永恒，但仍能产生准确的结果，而后者速度很快，但结果缺乏准确性。在本文中，我们介绍了一种新的抽样方法，基于聚类抽样，使利用ACE分析，以加快故障注入过程。在我们的实验中，我们证明了国家的最先进的故障注入技术，根据均匀分布产生随机故障，优于所提出的采样技术，从而使几个优势的准确性和评估时间。为了量化所带来的好处，我们分析了AMD Southern Islands GPU的微架构可靠性，其中存在影响6个基准测试的向量寄存器文件的单个位翻转。最重要的成就之一是，考虑到所有的基准，平均而言，我们是一个数量级的速度/更准确的均匀采样为基础的技术在非穷举故障注入活动的情况下，而超过两个数量级的情况下，穷举运动。
Computing capability demand has grown massively in recent years. Modern GPU chips are designed to deliver extreme performance for graphics and for data-parallel general purpose computing workloads (GPGPU computing) as well. Many GPGPU applications require high reliability, thus reliability evaluation has become a crucial step during their design. State-of-the-art techniques to assess the reliability of a system are fault injection and ACE analysis. The former can produce accurate results despite eternal time while the latter is very fast but it lacks accuracy of the results. In this paper we introduce a new sampling methodology based on cluster sampling that enables the exploitation of ACE analysis to accelerate the fault injection process. In our experiments we demonstrate that state-of-the-art fault injection techniques, generating random faults according to a uniform distribution, is outperformed by the proposed sampling technique, thus enabling several advantages in terms of accuracy and evaluation time. To quantify the introduced benefits we analyzed the micro-architecture reliability of an AMD Southern Islands GPU in presence of single bit upset affecting the vector register file for 6 benchmarks. One of the most important achievements is that considering all the benchmarks, on average, we are one order of magnitude faster/more accurate than uniform-sampling-based techniques in case of non exhaustive fault injection campaigns, while more than two orders of magnitude in case of exhaustive campaigns.