Software Tools for Performance Modeling of Parallel Programs

Martínez, D.; Blanco, Vicente; Boullón, Marcos; Dominguez, Jessica A.; Rodríguez, Casiano; Rivera, Francisco F.

doi:10.1109/ipdps.2007.370582

Cited by 7 publications

(2 citation statements)

References 17 publications

(21 reference statements)

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“…The TIA framework provides the user with a simple and powerful environment for analyzing the performance of HPC applications. In particular, the main objective of this framework is to obtain analytical expressions of the performance of measurable metrics as a function of the execution parameters.…”

Section: The Tia Frameworkmentioning

confidence: 99%

“…This criterion, widely used in the fields of biology and econometrics, is an objective tool that ranks the models according to their suitability for describing the experimental data. In , a preliminary version of this modeling method was introduced, which was loosely integrated on the Tools for Instrumentation and Analysis (TIA) modeling framework . The present work formalizes the description of the method, including new features such as a renewed procedure to interact with the user and its full integration on the TIA framework.…”

Section: Introductionmentioning

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: The Tia Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%