adding the conclution

[mpi-energy2.git] / Heter_paper.tex

diff --git a/Heter_paper.tex b/Heter_paper.tex
index 2fc854959ff4a138331b9011b0833c60010fe89b..6f2bd7abdb194e74b1d0d7bb94e5aca6d77a6105 100644 (file)
--- a/Heter_paper.tex
+++ b/Heter_paper.tex
@@ -76,7 +76,19 @@
 \maketitle
 
 \begin{abstract}
 \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}
 \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.
 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 
 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. 
 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. 


@@ -910,7 +922,7 @@ down the frequencies of some nodes have less effect on the performance.
 
 
 \subsection{The results for different power consumption scenarios}
 
 
 \subsection{The results for different power consumption scenarios}

-
+\label{sec.compare}

 The results of the previous section were obtained while using processors that consume during computation 
 an overall power which is 80\% composed of  dynamic power and 20\% of static power. In this section, 
 these ratios are changed and two new power scenarios are considered in order to evaluate how the proposed  
 The results of the previous section were obtained while using processors that consume during computation 
 an overall power which is 80\% composed of  dynamic power and 20\% of static power. In this section, 
 these ratios are changed and two new power scenarios are considered in order to evaluate how the proposed  


@@ -1028,7 +1040,7 @@ linearly related the execution time and the dynamic energy is related to the com
 the work presented in this paper is based on the execution time model. To verify this model, the predicted 
 execution time was compared to  the real execution time over Simgrid for all  the NAS parallel benchmarks 
 running class B on 8 or 9 nodes. The comparison showed that the proposed execution time model is very precise, 
 the work presented in this paper is based on the execution time model. To verify this model, the predicted 
 execution time was compared to  the real execution time over Simgrid for all  the NAS parallel benchmarks 
 running class B on 8 or 9 nodes. The comparison showed that the proposed execution time model is very precise, 

-the maximum normalized difference between  the predicted execution time  and the real execution time is equal 
+the maximum normalized difference between the predicted execution time  and the real execution time is equal 

 to 0.03 for all the NAS benchmarks.
 
 Since  the proposed algorithm is not an exact method and do not test all the possible solutions (vectors of scaling factors) 
 to 0.03 for all the NAS benchmarks.
 
 Since  the proposed algorithm is not an exact method and do not test all the possible solutions (vectors of scaling factors) 


@@ -1040,15 +1052,29 @@ for a heterogeneous cluster composed of four different types of nodes having the
 table~(\ref{table:platform}), it takes on average \np[ms]{0.04}  for 4 nodes and \np[ms]{0.15} on average for 144 nodes 
 to compute the best scaling factors vector.  The algorithm complexity is $O(F\cdot (N \cdot4) )$, where $F$ is the number 
 of iterations and $N$ is the number of computing nodes. The algorithm needs  from 12 to 20 iterations to select the best 
 table~(\ref{table:platform}), it takes on average \np[ms]{0.04}  for 4 nodes and \np[ms]{0.15} on average for 144 nodes 
 to compute the best scaling factors vector.  The algorithm complexity is $O(F\cdot (N \cdot4) )$, where $F$ is the number 
 of iterations and $N$ is the number of computing nodes. The algorithm needs  from 12 to 20 iterations to select the best 

-vector of frequency scaling factors that gives the results of the section (\ref{sec.res}).
+vector of frequency scaling factors that gives the results of the sections (\ref{sec.res}) and (\ref{sec.compare}).

 
 \section{Conclusion}
 \label{sec.concl}
 
 \section{Conclusion}
 \label{sec.concl}

-
+In this paper, we have presented a new online heterogeneous scaling algorithm
+that selects the best possible vector of frequency scaling factors. This vector 
+gives the maximum distance (optimal tradeoff) between the predicted energy and 
+the predicted performance curves. In addition, we developed a new energy model for measuring  
+and predicting the energy of distributed iterative applications running over heterogeneous 
+cluster. The proposed method evaluated on Simgrid/SMPI  simulator to built a heterogeneous 
+platform to executes NAS parallel benchmarks. The results of the experiments showed the ability of
+the proposed algorithm to changes its behaviour to selects different scaling factors  when 
+the number of computing nodes and both of the static and the dynamic powers are changed. 
+
+In the future, we plan to improve this method to apply on asynchronous  iterative applications 
+where each task does not wait the others tasks to finish there works. This leads us to develop a new 
+energy model to an asynchronous iterative applications, where the number of iterations is not 
+known in advance and depends on the global convergence of the iterative system.

 
 \section*{Acknowledgment}
 
 
 
 \section*{Acknowledgment}
 
 

+

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