Paper Info
Title
Using accurate AIC-based performance models to improve the scheduling of parallel applications
Abstract
Abstract Predictions based on analytical performance models can be used on efficient scheduling policies in order to select adequate resources for an optimal execution in terms of throughput and response time. However, developing accurate analytical models of parallel applications is a hard issue. The TIA (Tools for Instrumenting and Analysis) modeling framework provides an easy to use modeling method for obtaining analytical models of MPI applications. This method is based on modeling selection techniques and, in particular, on Akaike’s information criterion (AIC). In this paper, first the AIC-based performance model of the HPL benchmark is obtained using the TIA modeling framework. Then the use of this model for assessing the runtime estimation on different backfilling policies is analyzed in the GridSim simulator. The behavior of these simulations is compared with the equivalent simulations based on the theoretical model of the HPL provided by its developers.
Year
DOI
Venue
2011
10.1007/s11227-011-0589-1
The Journal of Supercomputing
Keywords
Field
DocType
Performance,Analytical models,Model selection,Backfilling schedulers
Akaike information criterion,Computer science,Scheduling (computing),Parallel computing,Response time,Model selection,Performance model,Throughput,Distributed computing
Journal
Volume
Issue
ISSN
58
3
0920-8542
Citations 
PageRank 
References 
2
0.39
5
Authors
6
Name
Order
Citations
PageRank
Diego Rodriguez Martinez1142.08
Julio L. Albín220.39
Tomás F. Pena36816.56
José C. Cabaleiro4103.44
Francisco F. Rivera517726.17
Vicente Blanco6415.64