基于图形处理单元的大规模n体程序中瞬态故障影响表征和数据损坏错误检测

Keun Soo YIM
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引用次数: 14

摘要

在n体程序中,如果在初始条件下或在某些计算步骤中发生错误,则模拟粒子的轨迹具有混沌模式。人们认为,模拟粒子的整体特性(例如,总能量)不太可能受到少量此类误差的影响。在本文中,我们提出了一个定量的分析在GPU设备的瞬态故障对模拟粒子的整体性质的影响。我们的实验表明,非控制数据中的一个比特错误可以以~2.1%的概率改变大规模n体程序的最终总能量。我们还发现损坏的总能量值有一定的偏差(例如,这些值不是正态分布),这可以用来减少期望的重新执行次数。在本文中,我们还提出了一种n体程序的数据错误检测技术,该技术利用了模拟物理模型中的两种类型的属性。所提出的技术和现有的基于冗余的技术一起覆盖了许多数据错误(例如,>97.5%),性能开销很小(例如,2.3%)。
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Characterization of Impact of Transient Faults and Detection of Data Corruption Errors in Large-Scale N-Body Programs Using Graphics Processing Units
In N-body programs, trajectories of simulated particles have chaotic patterns if errors are in the initial conditions or occur during some computation steps. It was believed that the global properties (e.g., total energy) of simulated particles are unlikely to be affected by a small number of such errors. In this paper, we present a quantitative analysis of the impact of transient faults in GPU devices on a global property of simulated particles. We experimentally show that a single-bit error in non-control data can change the final total energy of a large-scale N-body program with ~2.1% probability. We also find that the corrupted total energy values have certain biases (e.g., the values are not a normal distribution), which can be used to reduce the expected number of re-executions. In this paper, we also present a data error detection technique for N-body programs by utilizing two types of properties that hold in simulated physical models. The presented technique and an existing redundancy-based technique together cover many data errors (e.g., >97.5%) with a small performance overhead (e.g., 2.3%).
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