GPAW optimized for Blue Gene/P using hybrid programming

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Standard

GPAW optimized for Blue Gene/P using hybrid programming. / Kristensen, Mads Ruben Burgdorff; Happe, Hans Henrik; Vinter, Brian.

Proceedings of the 2009 IEEE International Symposium on Parallel & Distributed Processing. IEEE, 2009. s. 1-6.

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Harvard

Kristensen, MRB, Happe, HH & Vinter, B 2009, GPAW optimized for Blue Gene/P using hybrid programming. i Proceedings of the 2009 IEEE International Symposium on Parallel & Distributed Processing. IEEE, s. 1-6, International Parallel and Distributed Processing Symposium (IPDPS 2009), Rom, Italien, 23/05/2009. https://doi.org/10.1109/IPDPS.2009.5160936

APA

Kristensen, M. R. B., Happe, H. H., & Vinter, B. (2009). GPAW optimized for Blue Gene/P using hybrid programming. I Proceedings of the 2009 IEEE International Symposium on Parallel & Distributed Processing (s. 1-6). IEEE. https://doi.org/10.1109/IPDPS.2009.5160936

Vancouver

Kristensen MRB, Happe HH, Vinter B. GPAW optimized for Blue Gene/P using hybrid programming. I Proceedings of the 2009 IEEE International Symposium on Parallel & Distributed Processing. IEEE. 2009. s. 1-6 https://doi.org/10.1109/IPDPS.2009.5160936

Author

Kristensen, Mads Ruben Burgdorff ; Happe, Hans Henrik ; Vinter, Brian. / GPAW optimized for Blue Gene/P using hybrid programming. Proceedings of the 2009 IEEE International Symposium on Parallel & Distributed Processing. IEEE, 2009. s. 1-6

Bibtex

@inproceedings{97c920f0fabc11de825d000ea68e967b,
title = "GPAW optimized for Blue Gene/P using hybrid programming",
abstract = "In this work we present optimizations of a Grid-based projector-augmented wave method software, GPAW for the Blue Gene/P architecture. The improvements are achieved by exploring the advantage of shared and distributed memory programming also known as hybrid programming. The work focuses on optimizing a very time consuming operation in GPAW, the finite-different stencil operation, and different hybrid programming approaches are evaluated. The work succeeds in demonstrating a hybrid programming model which is clearly beneficial compared to the original flat programming model. In total an improvement of 1.94 compared to the original implementation is obtained. The results we demonstrate here are reasonably general and may be applied to other finite difference codes.",
keywords = "The Faculty of Science, HPC, Hybrid parallel programming, Parallel framework, GPAW",
author = "Kristensen, {Mads Ruben Burgdorff} and Happe, {Hans Henrik} and Brian Vinter",
year = "2009",
doi = "10.1109/IPDPS.2009.5160936",
language = "English",
pages = "1--6",
booktitle = "Proceedings of the 2009 IEEE International Symposium on Parallel & Distributed Processing",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - GPAW optimized for Blue Gene/P using hybrid programming

AU - Kristensen, Mads Ruben Burgdorff

AU - Happe, Hans Henrik

AU - Vinter, Brian

PY - 2009

Y1 - 2009

N2 - In this work we present optimizations of a Grid-based projector-augmented wave method software, GPAW for the Blue Gene/P architecture. The improvements are achieved by exploring the advantage of shared and distributed memory programming also known as hybrid programming. The work focuses on optimizing a very time consuming operation in GPAW, the finite-different stencil operation, and different hybrid programming approaches are evaluated. The work succeeds in demonstrating a hybrid programming model which is clearly beneficial compared to the original flat programming model. In total an improvement of 1.94 compared to the original implementation is obtained. The results we demonstrate here are reasonably general and may be applied to other finite difference codes.

AB - In this work we present optimizations of a Grid-based projector-augmented wave method software, GPAW for the Blue Gene/P architecture. The improvements are achieved by exploring the advantage of shared and distributed memory programming also known as hybrid programming. The work focuses on optimizing a very time consuming operation in GPAW, the finite-different stencil operation, and different hybrid programming approaches are evaluated. The work succeeds in demonstrating a hybrid programming model which is clearly beneficial compared to the original flat programming model. In total an improvement of 1.94 compared to the original implementation is obtained. The results we demonstrate here are reasonably general and may be applied to other finite difference codes.

KW - The Faculty of Science

KW - HPC

KW - Hybrid parallel programming

KW - Parallel framework

KW - GPAW

U2 - 10.1109/IPDPS.2009.5160936

DO - 10.1109/IPDPS.2009.5160936

M3 - Article in proceedings

SP - 1

EP - 6

BT - Proceedings of the 2009 IEEE International Symposium on Parallel & Distributed Processing

PB - IEEE

ER -

ID: 16811129