DocumentCode
228727
Title
Omnisc´IO: A Grammar-Based Approach to Spatial and Temporal I/O Patterns Prediction
Author
Dorier, Matthieu ; Ibrahim, Shadi ; Antoniu, Gabriel ; Ross, Robert
Author_Institution
ENS Rennes, IRISA, Rennes, France
fYear
2014
fDate
16-21 Nov. 2014
Firstpage
623
Lastpage
634
Abstract
The increasing gap between the computation performance of post-petascale machines and the performance of their I/O subsystem has motivated many I/O optimizations including prefetching, caching, and scheduling techniques. In order to further improve these techniques, modeling and predicting spatial and temporal I/O patterns of HPC applications as they run has became crucial. In this paper we present Omnisc´IO, an approach that builds a grammar-based model of the I/O behavior of HPC applications and uses it to predict when future I/O operations will occur, and where and how much data will be accessed. Omnisc´IO is transparently integrated into the POSIX and MPI I/O stacks and does not require any modification in applications or higher level I/O libraries. It works without any prior knowledge of the application and converges to accurate predictions within a couple of iterations only. Its implementation is efficient in both computation time and memory footprint.
Keywords
Unix; cache storage; grammars; input-output programs; message passing; parallel processing; scheduling; storage management; HPC applications; I/O optimizations; I/O subsystem; MPI I/O stacks; Omnisc´IO; POSIX stacks; caching techniques; grammar-based approach; post-petascale machines; prefetching techniques; scheduling techniques; spatial I/O pattern prediction; temporal I/O pattern prediction; Context; Grammar; Hidden Markov models; Libraries; Prediction algorithms; Predictive models; Prefetching; Exascale; Grammar; HPC; I/O; Omnisc´IO; Prediction; Storage;
fLanguage
English
Publisher
ieee
Conference_Titel
High Performance Computing, Networking, Storage and Analysis, SC14: International Conference for
Conference_Location
New Orleans, LA
Print_ISBN
978-1-4799-5499-5
Type
conf
DOI
10.1109/SC.2014.56
Filename
7013038
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