Abstract :
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%).
Keywords :
data handling; error detection; fault tolerant computing; graphics processing units; multiprocessing systems; GPU devices; chaotic patterns; data corruption error detection; data error detection technique; graphics processing units; large-scale N-body programs; noncontrol data; quantitative analysis; redundancy-based technique; simulated particle trajectory; simulated physical models; single-bit error; transient fault impact characterization; Circuit faults; Computational modeling; Computers; Detectors; Graphics processing units; Histograms; Kernel; Error detection; N-body problem; fault characterization; graphics processing unit; silent data corruption;