Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | ARCS 2016 - 29th International Conference on Architecture of Computing Systems |
| Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers Inc. |
| Seiten | 1-4 |
| Seitenumfang | 4 |
| ISBN (elektronisch) | 9783800741571 |
| Publikationsstatus | Veröffentlicht - 27 Juni 2016 |
| Veranstaltung | 29th International Conference on Architecture of Computing Systems, ARCS 2016 - Nuremberg, Deutschland Dauer: 4 Apr. 2016 → 7 Apr. 2016 |
Abstract
The adaptation of sequential algorithms for High Performance Computing (HPC) systems is determined by a tradeoff between algorithmic effectiveness (software) and communication frequency (hardware) of the parallel implementation (efficiency). To get a better understanding of the correlation, we define simple models for both, software and hardware, in order to dynamically find the best mapping parameters for the execution of the algorithm on the parallel system. For the evaluation of our method we look at population-based algorithms like the Particle Swarm Optimization Algorithm (PSO) for the solution of optimization problems. Different goals like best quality of the solution of the optimization problem for a given execution time or best execution time to find the optimum are defined by the user. Our method enables us to find the best parameters for the mapping which then results in an efficient and effective parallel implementation to achieve the user-defined goals on a High Performance Computing Cluster (HPCC).
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Angewandte Informatik
- Informatik (insg.)
- Hardware und Architektur
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ARCS 2016 - 29th International Conference on Architecture of Computing Systems. Institute of Electrical and Electronics Engineers Inc., 2016. S. 1-4.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Towards an Algorithm and Communication Cost Model for the Parallel Particle Swarm Optimization
AU - Shuka, Romeo
AU - Niemann, Sebastian
AU - Brehm, Jürgen
AU - Müller-Schloer, Christian
PY - 2016/6/27
Y1 - 2016/6/27
N2 - The adaptation of sequential algorithms for High Performance Computing (HPC) systems is determined by a tradeoff between algorithmic effectiveness (software) and communication frequency (hardware) of the parallel implementation (efficiency). To get a better understanding of the correlation, we define simple models for both, software and hardware, in order to dynamically find the best mapping parameters for the execution of the algorithm on the parallel system. For the evaluation of our method we look at population-based algorithms like the Particle Swarm Optimization Algorithm (PSO) for the solution of optimization problems. Different goals like best quality of the solution of the optimization problem for a given execution time or best execution time to find the optimum are defined by the user. Our method enables us to find the best parameters for the mapping which then results in an efficient and effective parallel implementation to achieve the user-defined goals on a High Performance Computing Cluster (HPCC).
AB - The adaptation of sequential algorithms for High Performance Computing (HPC) systems is determined by a tradeoff between algorithmic effectiveness (software) and communication frequency (hardware) of the parallel implementation (efficiency). To get a better understanding of the correlation, we define simple models for both, software and hardware, in order to dynamically find the best mapping parameters for the execution of the algorithm on the parallel system. For the evaluation of our method we look at population-based algorithms like the Particle Swarm Optimization Algorithm (PSO) for the solution of optimization problems. Different goals like best quality of the solution of the optimization problem for a given execution time or best execution time to find the optimum are defined by the user. Our method enables us to find the best parameters for the mapping which then results in an efficient and effective parallel implementation to achieve the user-defined goals on a High Performance Computing Cluster (HPCC).
KW - High Performance Computing
KW - Optimization algorithms
KW - Particle Swarm Optimization
UR - http://www.scopus.com/inward/record.url?scp=85045898877&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85045898877
SP - 1
EP - 4
BT - ARCS 2016 - 29th International Conference on Architecture of Computing Systems
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 29th International Conference on Architecture of Computing Systems, ARCS 2016
Y2 - 4 April 2016 through 7 April 2016
ER -