Characterization of Impact of Transient Faults and Detection of Data Corruption Errors in Large-Scale N-Body Programs Using Graphics Processing Units
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 pro-
grams 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%).
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 pro-
grams 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%).
Research Areas
