Characterization of Impact of Transient Faults and Detection of Data Corruption Errors in Large-Scale N-Body Programs Using Graphics Processing Units
Characterization of Impact of Transient Faults and Detection of Data Corruption Errors in Large-Scale N-Body Programs Using Graphics Processing Units
复制标题
使用图形处理单元的大规模 N 体程序中瞬态故障影响的表征和数据损坏错误的检测
DOI：
10.1109/ipdps.2014.55
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
Keun Soo YIM
中科院分区：
文献类型：
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作者：
Keun Soo YIM
在N体程序中，如果错误处于初始条件或在某些计算步骤中发生，则模拟粒子的轨迹具有混乱的模式。人们认为，模拟粒子的全局特性（例如，总能量）不太可能受到少数此类误差的影响。在本文中，我们对GPU设备中瞬态故障对模拟粒子全球性质的影响进行定量分析。我们通过实验表明，非控制数据中的单位误差可以改变概率约为2.1％的大规模N体计划的最终总能量。我们还发现，损坏的总能量值具有某些偏见（例如，值不是正态分布），可用于减少预期的重新分配数量。在本文中，我们还通过使用模拟物理模型中的两种类型的属性来提出针对N体程序的数据错误检测技术。提出的技术和现有基于冗余的技术涵盖了许多数据误差（例如，> 97.5％），其性能开销较小（例如2.3％）。
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%).