adding the conclution

[mpi-energy2.git] / Heter_paper.tex

diff --git a/Heter_paper.tex b/Heter_paper.tex
index 2fc854959ff4a138331b9011b0833c60010fe89b..260408c7c7393d4451ead29ccfabdecefa3fc081 100644 (file)
--- a/Heter_paper.tex
+++ b/Heter_paper.tex
@@ -76,57 +76,50 @@
 \maketitle
 
 \begin{abstract}
 \maketitle
 
 \begin{abstract}

-  
+Computing platforms are consuming more and more energy due to the increase of the number of nodes composing them. To minimize the operating costs of these platforms many techniques have been used. Dynamic voltage and frequency scaling (DVFS) is one of them, it reduces the frequency of a CPU to lower its energy consumption. However, lowering the frequency of a CPU might increase the execution time of an application running on that processor. Therefore, the frequency that gives the best  tradeoff between the energy consumption and the performance of an application must be selected. 
+
+In this paper, a new online frequencies selecting algorithm for heterogeneous platforms is presented. It selects the frequency that gives  the best tradeoff between energy saving and
+performance degradation, for each node computing the message passing iterative application. The algorithm has a small overhead and works without training or profiling.
+It uses a new energy model for message passing iterative applications running on a heterogeneous platform. 
+The proposed algorithm was evaluated  on the Simgrid simulator while running the NAS parallel benchmarks.
+The experiments demonstrated that it reduces the energy consumption up to 35\% while limiting the performance degradation as much as possible.

 \end{abstract}
 
 \section{Introduction}
 \label{sec.intro}
 \end{abstract}
 
 \section{Introduction}
 \label{sec.intro}

-Modern processors continue increasing in performance, 
-the CPUs constructors are competing to achieve maximum number 
-of floating point operations per second (FLOPS). 
-Thus, the energy consumption and the heat dissipation are increased 
-drastically according to this increase. Because the number of FLOPS 
-is more related to the power consumption of a CPU
-~\cite{Luley_Energy.efficiency.evaluation.and.benchmarking}.  
-As an example of the most power hungry cluster, Tianhe-2 became in 
-the top of the Top500 list in June 2014 \cite{TOP500_Supercomputers_Sites}. 
-It has more than 3 millions of cores and consumed more than 17.8 megawatts. 
+The need for more computing power is continually increasing. To partially satisfy this need, most supercomputers constructors just put more computing nodes in their platform. The resulting platform might achieve higher floating point operations per second (FLOPS), but the energy consumption and the heat dissipation are also increased. As an example, the chinese supercomputer Tianhe-2 had the highest FLOPS in November 2014 according to the Top500 list \cite{TOP500_Supercomputers_Sites}.  However, it was also the  most power hungry platform with its over 3 millions cores consuming around 17.8 megawatts.

 Moreover, according to the U.S. annual energy outlook 2014 
 \cite{U.S_Annual.Energy.Outlook.2014}, the price of energy for 1 megawatt-hour 
 was approximately equal to \$70. 
 Moreover, according to the U.S. annual energy outlook 2014 
 \cite{U.S_Annual.Energy.Outlook.2014}, the price of energy for 1 megawatt-hour 
 was approximately equal to \$70. 

-Therefore, we can consider the price of the energy consumption for the 
-Tianhe-2 platform is approximately more than \$10 millions  for 
-one year. For this reason, the heterogeneous clusters must be offer more 
-energy efficiency due to the increase in the energy cost and the environment 
-influences. Therefore, a green computing clusters with maximum number of 
-FLOPS per watt are required nowadays. For example, the GSIC center of Tokyo, 
+Therefore, the price of the energy consumed by the 
+Tianhe-2 platform is approximately more than \$10 millions each year. 
+The computing platforms must be more energy efficient and offer the highest number of FLOPS per watt possible, such as the TSUBAME-KFC at the GSIC center of Tokyo which  

 became the top of the Green500 list in June 2014 \cite{Green500_List}. 
 became the top of the Green500 list in June 2014 \cite{Green500_List}. 

-This heterogeneous platform has more than four thousand of  MFLOPS per watt. Dynamic 
-voltage and frequency scaling (DVFS) is a process used widely to reduce the energy 
-consumption of the processor. In heterogeneous clusters enabled DVFS, many researchers 
-used DVFS  in a different ways. DVFS can be minimized the energy consumption 
-but it leads to a disadvantage due to the increase in performance degradation. 
-Therefore,  researchers used different optimization strategies to overcame 
-this problem. The best tradeoff relation between the energy reduction and 
-performance degradation ratio is became a key challenges in a heterogeneous 
-platforms. In this paper we are propose a heterogeneous scaling algorithm  
-that selects the optimal vector of the frequency scaling factors for distributed 
-iterative application, producing maximum energy reduction against minimum 
-performance degradation ratio simultaneously. The algorithm has very small 
-overhead, works online and not needs for any training or profiling.  
+This heterogeneous platform executes more than four  GFLOPS per watt.
+
+ Besides hardware improvements, there are many software techniques to lower the energy consumption of these platforms, such as scheduling, DVFS, ... DVFS is a widely  used process to reduce the energy 
+consumption of a processor by lowering its frequency. \textbf{put a reference to DVFS} However, it also the reduces the number of FLOPS executed by the processor which might increase the execution time of the application running over that processor.
+Therefore,  researchers used different optimization strategies to select the frequency that gives the best tradeoff   between the energy reduction and 
+performance degradation ratio.
+\textbf{you should talk about the first paper here and say that the algorithm was applied to a homogeneous platform then define what is a heterogeneous platform, you can take it from the firdt paragraph in section 3 }
+
+In this paper, a frequency selecting algorithm is proposed. It  selects the vector of frequencies for a heterogeneous platform that runs a message passing iterative application,  that gives the maximum energy reduction and minimum 
+performance degradation ratio simultaneously. The algorithm has a very small 
+overhead, works online and does not need any training or profiling.  

 
 This paper is organized as follows: Section~\ref{sec.relwork} presents some
 related works from other authors.  Section~\ref{sec.exe} describes how the
 
 This paper is organized as follows: Section~\ref{sec.relwork} presents some
 related works from other authors.  Section~\ref{sec.exe} describes how the

-execution time of MPI programs can be predicted.  It also presents an energy
-model for heterogeneous platforms. Section~\ref{sec.compet} presents
+execution time of message passing programs can be predicted.  It also presents an energy
+model that predicts the energy consumption of an application running over a heterogeneous platform. Section~\ref{sec.compet} presents

 the energy-performance objective function that maximizes the reduction of energy
 consumption while minimizing the degradation of the program's performance.
 the energy-performance objective function that maximizes the reduction of energy
 consumption while minimizing the degradation of the program's performance.

-Section~\ref{sec.optim} details the proposed heterogeneous scaling algorithm.
-Section~\ref{sec.expe} presents the results of running  the NAS benchmarks on 
-the proposed heterogeneous platform. It also shows the comparison of three 
-different power scenarios and it verifies the precision of the proposed algorithm.  
+Section~\ref{sec.optim} details the proposed frequency selecting algorithm then the precision of the proposed algorithm is verified.\textbf{the verification should be put here}  
+Section~\ref{sec.expe} presents the results of applying the algorithm on  the NAS parallel benchmarks and executing them 
+on a heterogeneous platform. It also shows the results of running three 
+different power scenarios and comparing them. 

 Finally, we conclude in Section~\ref{sec.concl} with a summary and some future works.
 
 Finally, we conclude in Section~\ref{sec.concl} with a summary and some future works.
 

+\textbf{never use we in an article and the algorithm is not heterogeneous! you cannot use scaling factors before defining what they are.}

 \section{Related works}
 \label{sec.relwork}
 Energy reduction process for high performance clusters recently performed using 
 \section{Related works}
 \label{sec.relwork}
 Energy reduction process for high performance clusters recently performed using 


@@ -153,9 +146,9 @@ 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 +903,7 @@ 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 +1021,7 @@ 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 +1033,29 @@ 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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