| Reference : Low energy and high performance scheduling on scalable computing systems |
| Scientific congresses, symposiums and conference proceedings : Paper published in a book | |||
| Engineering, computing & technology : Computer science | |||
| http://hdl.handle.net/10993/15411 | |||
| Low energy and high performance scheduling on scalable computing systems | |
| English | |
Pecero, Johnatan  [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >] | |
| Bouvry, Pascal [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >] | |
| Barrios, Carlos J. [Industrial University of Santander, Colombia] | |
| 28-Aug-2010 | |
| Proceedings of the Latin-American Conference on High Performance Computing (CLCAR 2010) | |
| 1-8 | |
| Yes | |
| No | |
| International | |
| 978-85-7727-252-5 | |
| Latin-American Conference on High Performance Computing (CLCAR 2010) | |
| from 25-08-2010 to 28-08-2010 | |
| Gramado | |
| Brazil | |
| [en] Scalable computing systems ; Scheduling ; Performance of Systems ; Heterogeneous Computing ; Distributed Systems ; Energy Optimization | |
| [en] With the fast development of supercomputers, energy
consumption by large scale computer systems has become a major concern. How to reduce energy consumption is now a critical issue in designing high-performance computing systems. Moreover, reducing energy consumption for high-performance computing can bring various benefits such as, reduce monetary operating costs, increase system reliability, and reduction of environmental impacts. Therefore, in this paper we address the problem of scheduling precedence-constrained parallel applications on heterogeneous scalable computing systems with the objectives of minimizing finish time and reduce energy consumption. We provide a scheduling algorithm based on the best-effort idea that adopts dynamic voltage scaling (DVS) to reduce energy consumption. That is, the algorithm firstly looks for nearoptimal solutions employing a list-based scheduling algorithm to find the minimum finish time (besteffort). Then, a fast random local search algorithm that exploits voltage scaling is used to reduce the energy consumption of the generated schedule without any performance degradation. Simulation results on structured graphs representing real-world applications emphasize the interest of the proposed approach. | |
| Researchers | |
| http://hdl.handle.net/10993/15411 | |
| Latin-American Conference on High Performance Computing |
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