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Graphics processing units (GPUs) are increasingly common in both safety-critical and high-performance computing (HPC) applications. Some current supercomputers are composed of thousands of GPUs so the probability of device corruption becomes very high. Moreover, the GPU's parallel capabilities are very attractive for the automotive and aerospace markets, where reliability is a serious concern. In this paper, the neutron sensitivity of the modern GPU caches, and internal resources are experimentally evaluated. Various Duplication With Comparison strategies to reduce GPU radiation sensitivity are then presented and validated through radiation experiments. Threads should be carefully duplicated to avoid undesired errors on shared resources and to avoid the exacerbation of errors in critical resources such as the scheduler.

Most High Performance Computing (HPC) systems today are known as "power hungry" because they aim at computing speed regardless to energy consumption. Some scientific applications still claim more speed and the community expects to reach exascale by the end of the decade. Nevertheless, to reach exascale we need to search alternatives to cope with energy constraints. A promising step forward in this direction is the usage of low power processors such as ARM. ARM processors target low power consumption in contrast with Xeon that are conventional on HPC aiming at computing speed. This paper presents a comparison between ARM and Xeon to evaluate if ARM is the future building block to HPC. We choose to use time-to-solution, peak power, and energy-tosolution to evaluate both processors from the user's perspective. The results point that although ARM having lower peak power, Xeon has still a better tradeoff from the user's point-of-view.

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We present an in-depth analysis of transient faults effects on HPC applications in Intel Xeon Phi processors based on radiation experiments and high-level fault injection. Besides measuring the realistic error rates of Xeon Phi, we quantify Silent Data Corruption (SDCs) by correlating the distribution of corrupted elements in the output to the application's characteristics. We evaluate the benefits of imprecise computing for reducing the programs' error rate. For example, for HotSpot a 0.5% tolerance in the output value reduces the error rate by 85%.We inject different fault models to analyze the sensitivity of given applications. We show that portions of applications can be graded by different criticalities. For example, faults occurring in the middle of LUD execution, or in the Sort and Tree portions of CLAMR, are more critical than the remaining portions. Mitigation techniques can then be relaxed or hardened based on the criticality of the particular portions.

Abstract-In this paper, we evaluate the error criticality of radiation-induced errors on modern High-Performance Computing (HPC) accelerators (Intel Xeon Phi and NVIDIA K40) through a dedicated set of metrics. We show that, as long as imprecise computing is concerned, the simple mismatch detection is not sufficient to evaluate and compare the radiation sensitivity of HPC devices and algorithms. Our analysis quantifies and qualifies radiation effects on applications' output correlating the number of corrupted elements with their spatial locality. Also, we provide the mean relative error (dataset-wise) to evaluate radiation-induced error magnitude.We apply the selected metrics to experimental results obtained in various radiation test campaigns for a total of more than 400 hours of beam time per device. The amount of data we gathered allows us to evaluate the error criticality of a representative set of algorithms from HPC suites. Additionally, based on the characteristics of the tested algorithms, we draw generic reliability conclusions for broader classes of codes. We show that arithmetic operations are less critical for the K40, while Xeon Phi is more reliable when executing particles interactions solved through Finite Difference Methods. Finally, iterative stencil operations seem the most reliable on both architectures.