From: afanfakh Date: Tue, 18 Nov 2014 14:38:14 +0000 (+0100) Subject: adding the abstract X-Git-Tag: pdsec15_submission~63 X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/mpi-energy2.git/commitdiff_plain/ae038cfaec6fb1fc594a5527c92da40cef77a4b3 adding the abstract --- diff --git a/Heter_paper.tex b/Heter_paper.tex index 2fc8549..65232f0 100644 --- a/Heter_paper.tex +++ b/Heter_paper.tex @@ -76,7 +76,19 @@ \maketitle \begin{abstract} - +Green computing emphasizes the importance of energy conservation, minimizing the negative impact +on the environment while achieving high performance and minimizing operating costs. So, energy reduction +process in a high performance clusters it can be archived using dynamic voltage and frequency +scaling (DVFS) technique, through reducing the frequency of a CPU. Using DVFS to lower levels +result in a high increase in performance degradation ratio. Therefore selecting the best frequencies +must give the best possible tradeoff between the energy and the performance of parallel program. + +In this paper we present a new online heterogeneous scaling algorithm that selects the best vector +of frequency scaling factors. These factors give the best tradeoff between the energy saving and the +performance degradation. The algorithm has small overhead and works without training and profiling. +We developed a new energy model for distributed iterative application running on heterogeneous cluster. +The proposed algorithm experimented on Simgrid simulator that applying the NAS parallel benchmarks. +It reduces the energy consumption up to 35\% while limits the performance degradation as much as possible. \end{abstract} \section{Introduction} @@ -153,9 +165,9 @@ on CPUs. As an example of this works, Luley et al. cluster composed of Intel Xeon CPUs and NVIDIA GPUs. Their main goal is to determined the energy efficiency as a function of performance per watt, the best tradeoff is done when the performance per watt function is maximized. In the work of Kia Ma et al. -~\cite{KaiMa_Holistic.Approach.to.Energy.Efficiency.in.GPU-CPU}, They developed a scheduling +~\cite{KaiMa_Holistic.Approach.to.Energy.Efficiency.in.GPU-CPU}, they developed a scheduling algorithm to distributed different workloads proportional to the computing power of the node -to be executed on a CPU or a GPU, emphasize all tasks must be finished in the same time. +to be executed on CPU or GPU, emphasize all tasks must be finished in the same time. Recently, Rong et al.~\cite{Rong_Effects.of.DVFS.on.K20.GPU}, Their study explain that a heterogeneous clusters enabled DVFS using GPUs and CPUs gave better energy and performance efficiency than other clusters composed of only CPUs.