Omnisc'IO: A Grammar-Based Approach to Spatial and Temporal I/O Patterns Prediction - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2014

Omnisc'IO: A Grammar-Based Approach to Spatial and Temporal I/O Patterns Prediction

Shadi Ibrahim
Gabriel Antoniu
Robert Ross
MCS

Résumé

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. 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 any HPC application, and uses it to predict when future I/O operations will occur, 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 towards accurate predictions within a couple of iterations only. Its implementation is very efficient both in computation time and memory footprint.
Fichier principal
Vignette du fichier
paper.pdf (966.43 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01025670 , version 1 (24-07-2014)

Identifiants

  • HAL Id : hal-01025670 , version 1

Citer

Matthieu Dorier, Shadi Ibrahim, Gabriel Antoniu, Robert Ross. Omnisc'IO: A Grammar-Based Approach to Spatial and Temporal I/O Patterns Prediction. SC14 - International Conference for High Performance Computing, Networking, Storage and Analysis, IEEE, ACM, Nov 2014, New Orleans, United States. ⟨hal-01025670⟩
686 Consultations
633 Téléchargements

Partager

Gmail Mastodon Facebook X LinkedIn More