The increased need for computing capabilities and higher efficiency have stimulated industries to make available in the market novel architectures with increased complexity. The variety of codes that need to be executed combined with the complexity of novel architectures introduces challenges in the reliability evaluation of computing systems and applications. This paper compares the reliability behaviors of six different architectures (an Intel co-processor, three NVIDIA GPUs, an AMD APU, an embedded ARM) executing eight different codes. To support our evaluation, we present and discuss experimental beam data that covers a total of more than 352,000 years of natural exposure and fault-injection analysis based on a total of more than 120,000 injections. We first quantify both the Silent Data Corruptions and the Detected Unrecoverable Errors rates. Then, we qualify observed errors considering the difference between the corrupted and expected values as well as the portion of the output that has been corrupted. From these analyses, we identify the reliability characteristics which are related to the underlying hardware and the intrinsic behaviors of the executed code. Finally, we discuss the implications of the device- and code-dependent reliability behaviors for approximate computing. We analyze the benefits, in term of reduced error rate, of a relaxed output correctness.

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.