Predicting the best mapping for efficient exploitation of task and data parallelism

Fernando Guirado; Ana Ripoll; Concepció Roig; Xiao Yuan; Emilio Luque

Predicting the best mapping for efficient exploitation of task and data parallelism

Fernando Guirado, Ana Ripoll, Concepció Roig, Xiao Yuan, Emilio Luque

Research output: Contribution to journal › Article › Research › peer-review

Abstract

The detection and exploitation of different kinds of parallelism, task parallelism and data parallelism often leads to efficient parallel programs. This paper presents a simulation environment to predict the best mapping for the execution of message-passing applications on distributed systems. Using this environment, we evaluate the performance of an image processing application for the different parallelizing alternatives, and we propose the ways to improve its performance. © Springer-Verlag 2003.

Original language	English
Pages (from-to)	218-223
Journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	2790
Publication status	Published - 1 Dec 2004

Cite this

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title = "Predicting the best mapping for efficient exploitation of task and data parallelism",

abstract = "The detection and exploitation of different kinds of parallelism, task parallelism and data parallelism often leads to efficient parallel programs. This paper presents a simulation environment to predict the best mapping for the execution of message-passing applications on distributed systems. Using this environment, we evaluate the performance of an image processing application for the different parallelizing alternatives, and we propose the ways to improve its performance. {\textcopyright} Springer-Verlag 2003.",

author = "Fernando Guirado and Ana Ripoll and Concepci{\'o} Roig and Xiao Yuan and Emilio Luque",

year = "2004",

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Predicting the best mapping for efficient exploitation of task and data parallelism. / Guirado, Fernando; Ripoll, Ana; Roig, Concepció; Yuan, Xiao; Luque, Emilio.

In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 2790, 01.12.2004, p. 218-223.

Research output: Contribution to journal › Article › Research › peer-review

TY - JOUR

T1 - Predicting the best mapping for efficient exploitation of task and data parallelism

AU - Guirado, Fernando

AU - Ripoll, Ana

AU - Roig, Concepció

AU - Yuan, Xiao

AU - Luque, Emilio

PY - 2004/12/1

Y1 - 2004/12/1

N2 - The detection and exploitation of different kinds of parallelism, task parallelism and data parallelism often leads to efficient parallel programs. This paper presents a simulation environment to predict the best mapping for the execution of message-passing applications on distributed systems. Using this environment, we evaluate the performance of an image processing application for the different parallelizing alternatives, and we propose the ways to improve its performance. © Springer-Verlag 2003.

AB - The detection and exploitation of different kinds of parallelism, task parallelism and data parallelism often leads to efficient parallel programs. This paper presents a simulation environment to predict the best mapping for the execution of message-passing applications on distributed systems. Using this environment, we evaluate the performance of an image processing application for the different parallelizing alternatives, and we propose the ways to improve its performance. © Springer-Verlag 2003.

M3 - Article

VL - 2790

SP - 218

EP - 223

JO - Lecture Notes in Computer Science

JF - Lecture Notes in Computer Science

SN - 0302-9743

ER -

Predicting the best mapping for efficient exploitation of task and data parallelism

Abstract

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