Reference : Energy-aware scheduling of malleable HPC applications using a Particle Swarm optimise...
Scientific journals : Article
Engineering, computing & technology : Computer science
Computational Sciences
Energy-aware scheduling of malleable HPC applications using a Particle Swarm optimised greedy algorithm.
Mejri, Nesryne mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > CVI2 >]
Dupont, Briag Gerard Benjamin mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > CVI2 >]
Da Costa, Georges mailto [Université Paul Sabatier - Toulouse 3 - UPS > Institut de Recherche en Informatique de Toulouse > > Associate professor]
Sustainable Computing: Informatics and Systems
United States
[en] Energy efficiency ; Malleable jobs ; Particle Swarm Optimisation ; High performance computing ; Scheduling ; HPC
[en] 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.
Researchers ; Students

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