skip to main content


This content will become publicly available on February 1, 2019

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

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. Here in this paper, 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.
 [1] ;  [2] ;  [3] ;  [2] ;  [3] ;  [3] ;  [4] ;  [1] ;  [2]
  1. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  2. Argonne National Lab. (ANL), Argonne, IL (United States)
  3. Barcelona Supercomputing Center (Spain)
  4. Barcelona Supercomputing Center (Spain); Spanish National Research Council (CSIC), Madrid (Spain). IIIA - Artificial Intelligence Research Inst.
Publication Date:
Report Number(s):
Journal ID: ISSN 2210-5379; PII: S2210537917300896
Grant/Contract Number:
1619253; AC05-76RL01830; AC02-06CH11357; TIN2015-65316-P
Accepted Manuscript
Journal Name:
Sustainable Computing: Informatics and Systems
Additional Journal Information:
Journal Name: Sustainable Computing: Informatics and Systems; Journal ID: ISSN 2210-5379
Research Org:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org:
USDOE; National Science Foundation (NSF); European Union (EU)
Country of Publication:
United States
97 MATHEMATICS AND COMPUTING; Silent data corruptions; Support vector machines; HPC applications
OSTI Identifier: