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See detailUsing Data-flow analysis in MAS for power-aware HPC runs
Varrette, Sébastien UL; Danoy, Grégoire UL; Guzek, Mateusz UL et al

in Proc. of the IEEE Intl. Conf. on High Performance Computing and Simulation (HPCS'13) (2013)

Detailed reference viewed: 44 (2 UL)
See detailMemetic Algorithms for Energy-Aware Computation and Communications Optimization in Computing Clusters
Pecero, Johnatan UL; Dorronsoro, Bernabé UL; Guzek, Mateusz UL et al

in Ahmad, Ishfaq; Ranka, Sanjay (Eds.) Handbook energy-aware and green computing (2012)

Detailed reference viewed: 73 (5 UL)
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See detailImpact of Voltage Levels Number for Energy-aware Bi-objective DAG Scheduling for Multi-processors Systems
Guzek, Mateusz UL; Diaz, Cesar UL; Pecero, Johnatan UL et al

in Advances in Information Technology: 5th International Conference, IAIT 2012, Bangkok, Thailand, December 6-7, 2012, Proceedings (2012)

Detailed reference viewed: 53 (11 UL)
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See detailEnergy-Aware Scheduling of Parallel Applications with Multi-Objective Evolutionary Algorithm
Guzek, Mateusz UL; Pecero, Johnatan UL; Dorronsoro, Bernabé UL et al

in EVOLVE 2011, A bridge between Probability, Set Oriented Numerics and Evolutionary Computation, Bourglinster Castle, Luxembourg, May 25-27 2011 (2011)

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See detailEnergy-Aware Fast Scheduling Heuristics in Heterogeneous Computing Systems
Diaz, Cesar UL; Guzek, Mateusz UL; Pecero, Johnatan UL et al

in High Performance Computing and Simulation (HPCS), 2011 International Conference on (2011)

In heterogeneous computing systems it is crucial to sched- ule tasks in a manner that exploits the heterogeneity of the resources and applications to optimize systems perfor- mance. Moreover, the energy ... [more ▼]

In heterogeneous computing systems it is crucial to sched- ule tasks in a manner that exploits the heterogeneity of the resources and applications to optimize systems perfor- mance. Moreover, the energy efficiency in these systems is of a great interest due to different concerns such as opera- tional costs and environmental issues associated to carbon emissions. In this paper, we present a series of original low complexity energy efficient algorithms for scheduling. The main idea is to map a task to the machine that executes it fastest while the energy consumption is minimum. On the practical side, the set of experimental results showed that the proposed heuristics perform as efficiently as related ap- proaches, demonstrating their applicability for the consid- ered problem and its good scalability. [less ▲]

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See detailScalable and Energy-Efficient Scheduling Techniques for Large-Scale Systems
Diaz, Cesar UL; Guzek, Mateusz UL; Pecero, Johnatan UL et al

in Computer and Information Technology (CIT), 2011 IEEE 11th International Conference on (2011)

The scalability of a computing system can be identified by at least three components: (a) size, (b) geograph- ical distribution, and (c) administrative constraints. Newer paradigms, such as clouds, grids ... [more ▼]

The scalability of a computing system can be identified by at least three components: (a) size, (b) geograph- ical distribution, and (c) administrative constraints. Newer paradigms, such as clouds, grids, and clusters bring in more parameters to the aforementioned list, namely heterogeneity, energy consumption, and transparency. To optimize the per- formance of a computing system, it is manner that exploits heterogeneity and is scalable. Moreover, newer systems also demand energy efficiency as an integral part of schedulers. In this paper, we evaluate the behavior of low complexity energy- efficient algorithms for scheduling. The set of experimental results showed that the evaluated heuristics perform as effi- ciently as related approaches; demonstrating their applicability and scalability for the considered problem. [less ▲]

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See detailA Cellular Genetic Algorithm for Scheduling Applications and Energy-Aware Communication Optimization
Guzek, Mateusz UL; Pecero, Johnatan UL; Dorronsoro, Bernabé UL et al

in Proceedings of the 2010 International Conference on High Performance Computing & Simulation (2010)

Detailed reference viewed: 44 (2 UL)