Litcius/Paper detail

SpotSDC: Revealing the Silent Data Corruption Propagation in High-Performance Computing Systems

Zhimin Li, Harshitha Menon, Dan Maljovec, Yarden Livnat, Shusen Liu, Kathryn Mohror, Peer‐Timo Bremer, Valerio Pascucci

2020IEEE Transactions on Visualization and Computer Graphics18 citationsDOIOpen Access PDF

Abstract

The trend of rapid technology scaling is expected to make the hardware of high-performance computing (HPC) systems more susceptible to computational errors due to random bit flips. Some bit flips may cause a program to crash or have a minimal effect on the output, but others may lead to silent data corruption (SDC), i.e., undetected yet significant output errors. Classical fault injection analysis methods employ uniform sampling of random bit flips during program execution to derive a statistical resiliency profile. However, summarizing such fault injection result with sufficient detail is difficult, and understanding the behavior of the fault-corrupted program is still a challenge. In this article, we introduce SpotSDC, a visualization system to facilitate the analysis of a program's resilience to SDC. SpotSDC provides multiple perspectives at various levels of detail of the impact on the output relative to where in the source code the flipped bit occurs, which bit is flipped, and when during the execution it happens. SpotSDC also enables users to study the code protection and provide new insights to understand the behavior of a fault-injected program. Based on lessons learned, we demonstrate how what we found can improve the fault injection campaign method.

Topics & Concepts

Computer scienceFault injectionCrashResilience (materials science)VisualizationFault toleranceComputer engineeringFault (geology)Distributed computingSoftware fault toleranceParallel computingSoftwareData miningOperating systemThermodynamicsSeismologyPhysicsGeologyRadiation Effects in ElectronicsSoftware System Performance and ReliabilityAdvanced Malware Detection Techniques