Paper Info
Title
Towards End-to-end SDC Detection for HPC Applications Equipped with Lossy Compression
Abstract
Data reduction techniques have been widely demanded and used by large-scale high performance computing (HPC) applications because of vast volumes of data to be produced and stored for post-analysis. Due to very limited compression ratios of lossless compressors, error-bounded lossy compression has become an indispensable part in many HPC applications nowadays, because it can significantly reduce science data volume with user-acceptable data distortion. Since the large-scale HPC applications equipped with lossy compression techniques always need to deal with vast volume of data, soft errors or silent data corruptions (SDC) are non-negligible. Although SDC detection techniques have been studied for years, no studies were performed toward the HPC applications with lossy compression, leaving a significant gap between these applications and confidence of execution results. To fill this gap, this paper proposes a couple of SDC detection strategies for scientific simulations with lossy compression. Experimental results on 4 widely used scientific simulation datasets show promising detection ability could be still obtained with two popular lossy compressors. Our parallel experiments with up to 1,024 cores confirm that the time overheads could be limited within 7.9%.
Year
DOI
Venue
2020
10.1109/CLUSTER49012.2020.00043
2020 IEEE International Conference on Cluster Computing (CLUSTER)
Keywords
DocType
ISSN
user-acceptable data distortion,large-scale HPC applications,lossy compression techniques,silent data corruptions,SDC detection techniques,scientific simulation datasets,HPC applications,data reduction techniques,large-scale high performance computing applications,compression ratios,error-bounded lossy compression,science data volume,end-to-end SDC detection,post-analysis,soft errors
Conference
1552-5244
ISBN
Citations 
PageRank 
978-1-7281-6678-0
1
0.35
References 
Authors
24
6
Name
Order
Citations
PageRank
Sihuan Li1656.01
Sheng Di273755.88
Kai Zhao3194.32
Xin Liang410712.74
Zizhong Chen592469.93
Franck Cappello63775251.47