Exploring the capabilities of support vector machines in detecting silent data corruptions

Omer Subasi, Sheng Di, Leonardo Bautista-Gomez, Prasanna Balaprakash, Osman Unsal, Jesus Labarta, Adrian Cristal, Sriram Krishnamoorthy, Franck Cappello

Research output: Contribution to journalArticlepeer-review


As the exascale era approaches, the increasing capacity of high-performance computing (HPC) systems with targeted power and energy budget goals introduces significant challenges in reliability. Silent data corruptions (SDCs), or silent errors, are one of the major sources that corrupt the execution results of HPC applications without being detected. In this work, we explore a set of novel SDC detectors – by leveraging epsilon-insensitive support vector machine regression – to detect SDCs that occur in HPC applications. The key contributions are threefold. (1) Our exploration takes temporal, spatial, and spatiotemporal features into account and analyzes different detectors based on different features. (2) We provide an in-depth study on the detection ability and performance with different parameters, and we optimize the detection range carefully. (3) Experiments with eight real-world HPC applications show that support-vector-machine-based detectors can achieve detection sensitivity (i.e., recall) up to 99% yet suffer a less than 1% false positive rate for most cases. Our detectors incur low performance overhead, 5% on average, for all benchmarks studied in this work.

Original languageEnglish (US)
Pages (from-to)277-290
Number of pages14
JournalSustainable Computing: Informatics and Systems
StatePublished - Sep 2018
Externally publishedYes


  • HPC applications
  • Silent data corruptions
  • Support vector machines

ASJC Scopus subject areas

  • General Computer Science
  • Electrical and Electronic Engineering


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