X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/mpi-energy2.git/blobdiff_plain/c9875e5d70672da61c5cdc75fac90dad56cb1b04..ae038cfaec6fb1fc594a5527c92da40cef77a4b3:/Heter_paper.tex

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. 
