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UID:submissions.supercomputing.org_SC25_sess533_post201@linklings.com
SUMMARY:Detecting Silent Data Corruption in Sparse Matrices Using Hardware
  Performance Counters
DESCRIPTION:Minseop Choi, Orlando Arias, and Seung Woo Son (University of 
 Massachusetts Lowell)\n\nHigh performance computing (HPC) systems frequent
 ly execute large-scale sparse matrix computations in scientific and engine
 ering domains. These workloads are susceptible to silent data corruptions 
 (SDCs)—undetected faults that can alter results without triggering errors—
 posing a significant risk to computational integrity. In this work, we sho
 w how injected errors in sparse matrices propagate during repeated sparse 
 matrix-vector multiplication (SpMV) executions and evaluate whether hardwa
 re performance counter (PMC) patterns can be used to detect such corruptio
 ns. We conduct controlled experiments with Gaussian noise injection at var
 ying magnitudes and injection rates, record hardware counter values using 
 the Linux perf tool, and train a decision tree classifier to distinguish c
 orrupted runs from clean runs. Experiments on four real-world matrices fro
 m the SuiteSparse Matrix Collection yield detection accuracies around 90%–
 99% with under 2% runtime overhead. The results confirm that PMC-based cla
 ssification is a viable approach for lightweight SDC detection.\n\nTag: Re
 search & ACM SRC Posters\n\nRegistration Category: Technical Program Reg P
 ass\n\nSession Chairs: Kento Sato (RIKEN Center for Computational Science 
 (R-CCS)); Chris Schlipalius (Pawsey Supercomputing Research Centre; Common
 wealth Scientific and Industrial Research Organisation (CSIRO), Australia)
 ; and Anja Gerbes (Georg-August-Universität Göttingen)\n\n
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