2013Procedia Computer ScienceOpen access

A Tool for Selecting the Right Target Machine for Parallel Scientific Applications

Javier Panadero, Alvaro Wong, Dolores Rexachs, Emilio Luque

Open full text 11 citations

Abstract

Analyzing and predicting performance in parallel applications is a great challenge for scientific programmers due to its com- plexity. Analyzing parallel application behavior is not a trivial process and it requires spending a large amount of time and effort to understand the behavior of the application algorithms during execution. We have developed PAS2P toolkit from PAS2P methodology. This methodology strives to characterize the behavior of MPI applications to identify and extract repre- sentative phases and create a signature, which will be used to analyze the application behavior and predict its execution time in different target systems. Applying this methodology is a non-trivial process for users, for this reason we have developed the proposal toolkit, which allows users to make the whole process, from creating a signature to executing it on target systems, in user-space in an easy and fully automatic way. PAS2P toolkit has been validated, making clear the advantages of the signature, with its execution time being much lower than the whole application execution time (around 7% of the total execution time), with a high quality prediction of around 96%.

Open-access reader

About this research paper

What this paper is about

Analyzing and predicting performance in parallel applications is a great challenge for scientific programmers due to its com- plexity. Analyzing parallel application behavior is not a trivial process and it requires spending a large amount of time and effort to understand the behavior of the application algorithms during execution. We have developed PAS2P toolkit from PAS2P methodology. This methodology strives to characterize the behavior of MPI applications to identify and extract repre- sentative phases and create a signature, which will be used to analyze the application behavior and predict its execution time in different target systems. Applying this methodology is a non-trivial process for users, for this reason we have developed the proposal toolkit, which allows users to make the whole process, from creating a signature to executing it on target systems, in user-space in an easy and fully automatic way. PAS2P toolkit has been validated, making clear the advantages of the signature, with its execution time being much lower than the whole application execution time (around 7% of the total execution time), with a high quality prediction of around 96%.

Why it matters

OpenAlex reports 11 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

Analyzing and predicting performance in parallel applications is a great challenge for scientific programmers due to its com- plexity. Analyzing parallel application behavior is not a trivial process and it requires spending a large amount of time and effort to understand the behavior of the application algorithms during execution. We have developed PAS2P toolkit from PAS2P methodology. This methodology strives to characterize the behavior of MPI applications to identify and extract repre- sentative phases and create a signature, which will be used to analyze the application behavior and predict its execution time in different target systems. Applying this methodology is a non-trivial process for users, for this reason we have developed the proposal toolkit, which allows users to make the whole process, from creating a signature to executing it on target systems, in user-space in an easy and fully automatic way. PAS2P toolkit has been validated, making clear the advantages of the signature, with its execution time being much lower than the whole application execution time (around 7% of the total execution time), with a high quality prediction of around 96%.

Key concepts: Computer science, Signature (topology), Process (computing), Execution time, Quality (philosophy), Distributed computing, Programming language, Geometry

Related papers

Back to paper searchBrowse research topicsOriginal source
A Tool for Selecting the Right Target Machine for Parallel Scientific Applications — Research Paper | ScholarLens