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Job Level Communication-Avoiding Detection and Correction of Silent Data Corruption in HPC Applications
DescriptionDetecting and correcting Silent Data Corruption (SDC) is of high interest for many HPC applications due to the dramatic consequences such undetected computation errors can have. Additionally, going into the exascale era of computing, SDC error rates are only increasing with growing system sizes. State of the art methods based on instruction duplication suffer from only partial error coverage, significant synchronization overhead and strong coupling of computation and validation.
This work proposes a novel communication-avoiding approach of detecting and mitigating SDCs at the job level within the workload manager, assuming a directed acyclic graph (DAG) job model. Each job only communicates a locally generated output data hash. Computation and validation are decoupled as separately schedulable jobs and dependency stalling is avoided with a special error recovery method. The implementation of this project within the SLURM workload manager is in progress and key design aspects are outlined.
This work proposes a novel communication-avoiding approach of detecting and mitigating SDCs at the job level within the workload manager, assuming a directed acyclic graph (DAG) job model. Each job only communicates a locally generated output data hash. Computation and validation are decoupled as separately schedulable jobs and dependency stalling is avoided with a special error recovery method. The implementation of this project within the SLURM workload manager is in progress and key design aspects are outlined.
Event Type
ACM Student Research Competition: Graduate Poster
ACM Student Research Competition: Undergraduate Poster
Posters
TimeTuesday, 14 November 202310am - 5pm MST
LocationDEF Concourse
TP
XO/EX