Multicore Challenges and Benefits for High Performance Scientific Computing
Until recently, performance gains in processors were achieved largely by improvements in clock speeds and instruction level parallelism. Thus, applications could obtain performance increases with relatively minor changes by upgrading to the latest generation of computing hardware. Currently, however...
Main Authors: | , |
---|---|
Format: | Article |
Language: | English |
Published: |
Hindawi Limited
2008-01-01
|
Series: | Scientific Programming |
Online Access: | http://dx.doi.org/10.3233/SPR-2008-0260 |
id |
doaj-2663a1ca70e048029a7c7fb39da571cc |
---|---|
record_format |
Article |
spelling |
doaj-2663a1ca70e048029a7c7fb39da571cc2021-07-02T04:10:40ZengHindawi LimitedScientific Programming1058-92441875-919X2008-01-0116427728510.3233/SPR-2008-0260Multicore Challenges and Benefits for High Performance Scientific ComputingIda M.B. Nielsen0Curtis L. Janssen1Sandia National Laboratories, P.O. Box 969, Livermore, CA 94551, USASandia National Laboratories, P.O. Box 969, Livermore, CA 94551, USAUntil recently, performance gains in processors were achieved largely by improvements in clock speeds and instruction level parallelism. Thus, applications could obtain performance increases with relatively minor changes by upgrading to the latest generation of computing hardware. Currently, however, processor performance improvements are realized by using multicore technology and hardware support for multiple threads within each core, and taking full advantage of this technology to improve the performance of applications requires exposure of extreme levels of software parallelism. We will here discuss the architecture of parallel computers constructed from many multicore chips as well as techniques for managing the complexity of programming such computers, including the hybrid message-passing/multi-threading programming model. We will illustrate these ideas with a hybrid distributed memory matrix multiply and a quantum chemistry algorithm for energy computation using Møller–Plesset perturbation theory.http://dx.doi.org/10.3233/SPR-2008-0260 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ida M.B. Nielsen Curtis L. Janssen |
spellingShingle |
Ida M.B. Nielsen Curtis L. Janssen Multicore Challenges and Benefits for High Performance Scientific Computing Scientific Programming |
author_facet |
Ida M.B. Nielsen Curtis L. Janssen |
author_sort |
Ida M.B. Nielsen |
title |
Multicore Challenges and Benefits for High Performance Scientific Computing |
title_short |
Multicore Challenges and Benefits for High Performance Scientific Computing |
title_full |
Multicore Challenges and Benefits for High Performance Scientific Computing |
title_fullStr |
Multicore Challenges and Benefits for High Performance Scientific Computing |
title_full_unstemmed |
Multicore Challenges and Benefits for High Performance Scientific Computing |
title_sort |
multicore challenges and benefits for high performance scientific computing |
publisher |
Hindawi Limited |
series |
Scientific Programming |
issn |
1058-9244 1875-919X |
publishDate |
2008-01-01 |
description |
Until recently, performance gains in processors were achieved largely by improvements in clock speeds and instruction level parallelism. Thus, applications could obtain performance increases with relatively minor changes by upgrading to the latest generation of computing hardware. Currently, however, processor performance improvements are realized by using multicore technology and hardware support for multiple threads within each core, and taking full advantage of this technology to improve the performance of applications requires exposure of extreme levels of software parallelism. We will here discuss the architecture of parallel computers constructed from many multicore chips as well as techniques for managing the complexity of programming such computers, including the hybrid message-passing/multi-threading programming model. We will illustrate these ideas with a hybrid distributed memory matrix multiply and a quantum chemistry algorithm for energy computation using Møller–Plesset perturbation theory. |
url |
http://dx.doi.org/10.3233/SPR-2008-0260 |
work_keys_str_mv |
AT idambnielsen multicorechallengesandbenefitsforhighperformancescientificcomputing AT curtisljanssen multicorechallengesandbenefitsforhighperformancescientificcomputing |
_version_ |
1721340558116388864 |