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See detailEnergy-aware scheduling of malleable HPC applications using a Particle Swarm optimised greedy algorithm.
Mejri, Nesryne UL; Dupont, Briag Gerard Benjamin UL; Da Costa, Georges

in Sustainable Computing: Informatics and Systems (2020)

The scheduling of parallel tasks is a topic that has received a lot of attention in recent years, in particular, due to the development of larger HPC clusters. It is regarded as an interesting problem ... [more ▼]

The scheduling of parallel tasks is a topic that has received a lot of attention in recent years, in particular, due to the development of larger HPC clusters. It is regarded as an interesting problem because when combined with performant hardware, it ensures fast and efficient computing. However, it comes with a cost. The growing number of HPC clusters entails a greater global energy consumption which has a clear negative environmental impact. A green solution is thus required to find a compromise between energy-saving and high-performance computing within those clusters. In this paper, we evaluate the use of malleable jobs and idle servers powering off as a way to reduce both jobs mean stretch time and servers average power consumption. Malleable jobs have the particularity that the number of allocated servers can be changed during runtime. We present an energy-aware greedy algorithm with Particle Swarm Optimised parameters as a possible solution to schedule malleable jobs. An in-depth evaluation of the approach is then outlined using results from a simulator that was developed to handle malleable jobs. The results show that the use of malleable tasks can lead to an improved performance in terms of power consumption. We believe that our results open the door for further investigations on using malleable jobs models coupled with the energy-saving aspect. [less ▲]

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See detailEnergy aware ultrascale systems
Oleksiak, Ariel; Lefèvre, Laurent; Alonso, Pedro et al

in Carretero, J.; Jeannot, E.; Zomaya, A.Y. (Eds.) Ultrascale Computing Systems (2019)

Energy consumption is one of the main limiting factors for the design of ultrascale infrastructures. Multi-level hardware and software optimizations must be designed and explored in order to reduce energy ... [more ▼]

Energy consumption is one of the main limiting factors for the design of ultrascale infrastructures. Multi-level hardware and software optimizations must be designed and explored in order to reduce energy consumption for these largescale equipment. This chapter addresses the issue of energy efficiency of ultrascale systems in front of other quality metrics. The goal of this chapter is to explore the design of metrics, analysis, frameworks and tools for putting energy awareness and energy efficiency at the next stage. Significant emphasis will be placed on the idea of “energy complexity,” reflecting the synergies between energy efficiency and quality of service, resilience and performance, by studying computation power, communication/data sharing power, data access power, algorithm energy consumption, etc. [less ▲]

Detailed reference viewed: 77 (0 UL)