Details
| Original language | English |
|---|---|
| Pages (from-to) | 187-191 |
| Number of pages | 5 |
| Journal | Proceedings of the International Parallel Processing Symposium, IPPS |
| Publication status | Published - 1997 |
| Event | 11th International Parallel Processing Symposium, IPPS 97 - Geneva, Switzerland Duration: 1 Apr 1997 → 5 Apr 1997 |
Abstract
Today's massively parallel machines are typically message-passing systems consisting of hundreds or thousands of processors. Implementing parallel applications efficiently in this environment is a challenging task, and poor parallel design decisions can be expensive to correct. Tools and techniques that allow the fast and accurate evaluation of different parallelization strategies would significantly improve the productivity of application developers and increase throughput on parallel architectures. This paper investigates one of the major issues in building tools to compare parallelization strategies: determining what type of performance models of the application code and of the computer system are sufficient for a fast and accurate comparison of different strategies. The paper is built around a case study employing the Performance Prediction Tool (PerPreT) to predict performance of the Parallel Spectral Transform Shallow Water Model code (PSTSWM) on the Intel Paragon.
ASJC Scopus subject areas
- Computer Science(all)
- Hardware and Architecture
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In: Proceedings of the International Parallel Processing Symposium, IPPS, 1997, p. 187-191.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - Performance prediction for complex parallel applications
AU - Brehm, Juergen
AU - Worley, Patrick H.
PY - 1997
Y1 - 1997
N2 - Today's massively parallel machines are typically message-passing systems consisting of hundreds or thousands of processors. Implementing parallel applications efficiently in this environment is a challenging task, and poor parallel design decisions can be expensive to correct. Tools and techniques that allow the fast and accurate evaluation of different parallelization strategies would significantly improve the productivity of application developers and increase throughput on parallel architectures. This paper investigates one of the major issues in building tools to compare parallelization strategies: determining what type of performance models of the application code and of the computer system are sufficient for a fast and accurate comparison of different strategies. The paper is built around a case study employing the Performance Prediction Tool (PerPreT) to predict performance of the Parallel Spectral Transform Shallow Water Model code (PSTSWM) on the Intel Paragon.
AB - Today's massively parallel machines are typically message-passing systems consisting of hundreds or thousands of processors. Implementing parallel applications efficiently in this environment is a challenging task, and poor parallel design decisions can be expensive to correct. Tools and techniques that allow the fast and accurate evaluation of different parallelization strategies would significantly improve the productivity of application developers and increase throughput on parallel architectures. This paper investigates one of the major issues in building tools to compare parallelization strategies: determining what type of performance models of the application code and of the computer system are sufficient for a fast and accurate comparison of different strategies. The paper is built around a case study employing the Performance Prediction Tool (PerPreT) to predict performance of the Parallel Spectral Transform Shallow Water Model code (PSTSWM) on the Intel Paragon.
UR - http://www.scopus.com/inward/record.url?scp=0030714255&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:0030714255
SP - 187
EP - 191
JO - Proceedings of the International Parallel Processing Symposium, IPPS
JF - Proceedings of the International Parallel Processing Symposium, IPPS
SN - 1063-7133
T2 - 11th International Parallel Processing Symposium, IPPS 97
Y2 - 1 April 1997 through 5 April 1997
ER -