Using accurate AIC-based performance models to improve the scheduling of parallel applications

Martínez, D.; Albín, Julio L.; Pena, Tomás F.; Dominguez, Jessica A.; Rivera, Francisco F.; Blanco, Vicente

doi:10.1007/s11227-011-0589-1

Cited by 2 publications

(2 citation statements)

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“…In addition, it provides some statistical information Copyright to help the user to decide the suitability of the model. This method had been proven in different studies as, for example, to automatically obtain accurate time predictions that allow to improve the performance of backfilling policies for job scheduling in clusters [3]. An appropriate set of CM is the key to guarantee the quality of the selection process.…”

Section: Model Selection For the Performance Of Applications On Tiamentioning

confidence: 99%

“…Thus, although they are generally less accurate than simulations, they have the advantage of being able to evaluate the model in much less time. This feature is essential for effective predictions in problems of scheduling and dynamic load balance [2,3]. Due to the fact that they are based on a concise characterization of the problem, analytical models also provides an abstract view of both hardware and software and their combination.Different analytical modeling approaches for parallel systems have been considered in the literature [4,5].…”

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confidence: 99%

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Modeling the performance of parallel applications using model selection techniques

Martínez

Blanco

Dominguez

et al. 2013

Concurrency and Computation

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SUMMARYNowadays, parallel architectures are changing so fast that there is a need for scalable and efficient tools to analyze and predict the performance of parallel applications. Analytical models are proved to be a useful approximation for characterizing parallel algorithms, but developing accurate analytical models is a hard issue, and, in general, they provide coarse performance predictions due to their intrinsic lack of accuracy. In this paper, we describe in detail the Tools for Instrumentation and Analysis (TIA) framework, an easy-touse tool that automatically obtains accurate performance models by means of analytical expressions. This framework automatizes most of its internal tasks, reducing opportunities for human error, and it only requires the user to focus on the metrics and execution parameters that might influence the performance, those that should be considered in the modeling process. Its main advantage over other tools is that TIA uses model selection techniques that allow the automation of the modeling process. As a case of study, the use of TIA to obtain analytical models of different implementations of the broadcast collective communication in a cluster of multicores is shown. The results obtained by TIA are evaluated and compared with theoretical approaches based on the LogGP model.

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Section: Model Selection For the Performance Of Applications On Tiamentioning

confidence: 99%

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confidence: 99%