X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/mpi-energy2.git/blobdiff_plain/db7215cdaa5f7053e2332a73837622710a03ee23..208a7faf6fcd36c659b1ae7043b67ccc39ea2a9c:/mpi-energy2-extension/Heter_paper.tex diff --git a/mpi-energy2-extension/Heter_paper.tex b/mpi-energy2-extension/Heter_paper.tex index 9592bff..d313a42 100644 --- a/mpi-energy2-extension/Heter_paper.tex +++ b/mpi-energy2-extension/Heter_paper.tex @@ -85,6 +85,35 @@ \maketitle + +\begin{abstract} + + In recent years, green computing topic has being became an important topic in + the domain of the research. The increase in computing power of the computing + platforms is increased the energy consumption and the carbon dioxide emissions. + Many techniques have being used to minimize the cost of the energy consumption + and reduce environmental pollution. Dynamic voltage and frequency scaling (DVFS) + is one of these techniques. It used to reduce the power consumption of the CPU + while computing by lowering its frequency. Moreover, lowering the frequency of + a CPU may increase the execution time of an application running on that + processor. Therefore, the frequency that gives the best trade-off between + the energy consumption and the performance of an application must be selected. + + In this paper, a new online frequency selecting algorithm for heterogeneous + grid (heterogeneous CPUs) is presented. It selects the frequencies and tries to give the best + trade-off 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 + grid. The proposed algorithm is evaluated on real testbed, grid'5000 platform, while + running the NAS parallel benchmarks. The experiments show that it reduces the + energy consumption on average up to \np[\%]{30} while declines the performance + on average by \np[\%]{3} only for the same instance. Finally, the algorithm is + compared to an existing method, the comparison results show that it outperforms the + latter in term of energy and performance trade-off. +\end{abstract} + + \section{Introduction} \label{sec.intro} \textcolor{blue}{ @@ -97,7 +126,7 @@ Tianhe-2 had the highest FLOPS in June 2015 according to the Top500 list \cite{TOP500_Supercomputers_Sites}. However, it was also the most power hungry platform with its over 3 million cores consuming around 17.8 megawatts. Moreover, according to the U.S. annual energy outlook 2015 -\cite{U.S_Annual.Energy.Outlook.2014}, the price of energy for 1 megawatt-hour +\cite{U.S_Annual.Energy.Outlook.2015}, the price of energy for 1 megawatt-hour was approximately equal to \$70. Therefore, the price of the energy consumed by the Tianhe-2 platform is approximately more than \$10 million each year. The computing platforms must be more energy efficient and offer the highest number @@ -107,6 +136,7 @@ This heterogeneous platform executes more than 7 GFLOPS per watt while consuming 50.32 kilowatts. } +\textcolor{blue}{ Besides platform improvements, there are many software and hardware techniques to lower the energy consumption of these platforms, such as scheduling, DVFS, \dots{} DVFS is a widely used process to reduce the energy consumption of a @@ -120,12 +150,14 @@ trade-off between the energy reduction and performance degradation ratio. In the energy consumption of message passing iterative applications running over homogeneous and heterogeneous clusters respectively. The results of the experiments show significant energy -consumption reductions. In this paper, a new frequency selecting algorithm -adapted for heterogeneous platform is presented. It selects the vector of +consumption reductions. All the experimental results were conducted over +Simgrid simulator \cite{SimGrid}, which offers easy tools to create a homogeneous and heterogeneous platforms. In this paper, a new frequencies selecting algorithm +adapted for heterogeneous grid platform is presented and executed over real testbed, +the grid'5000 platform \cite{grid5000}. It selects the vector of frequencies, for a heterogeneous grid platform running a message passing iterative application, that simultaneously tries to offer the maximum energy reduction and minimum performance degradation ratio. The algorithm has a very small overhead, -works online and does not need any training or profiling. +works online and does not need any training or profiling.} \textcolor{blue}{ This paper is organized as follows: Section~\ref{sec.relwork} presents some @@ -443,12 +475,15 @@ appropriate frequency scaling factor for each processor while considering the characteristics of each processor (computation power, range of frequencies, dynamic and static powers) and the task executed (computation/communication ratio). The aim being to reduce the overall energy consumption and to avoid -increasing significantly the execution time. In our previous -work~\cite{Our_first_paper,pdsec2015}, we proposed a method that selects the optimal -frequency scaling factor for a homogeneous and heterogeneous clusters executing a message passing +increasing significantly the execution time. +\textcolor{blue}{ In our previous +works~\cite{Our_first_paper} and \cite{pdsec2015}, we proposed a methods that select the optimal +frequency scaling factors for a homogeneous and a heterogeneous clusters respectively. +Both of the two methods executing a message passing iterative synchronous application while giving the best trade-off between the energy consumption and the performance for such applications. In this work we -are interested in heterogeneous grid as described above. Due to the +are interested in heterogeneous grid as described above.} +Due to the heterogeneity of the processors, a vector of scaling factors should be selected and it must give the best trade-off between energy consumption and performance. @@ -1037,7 +1072,7 @@ to 10\% and are higher than those executed over the one site multi-cores scenari which on average is equal to 7\%. \textcolor{blue}{ -The performance degradation percentages over one site multi-cores is lower because the computations to communications ratio is decreased. Therefore, selecting small +The performance degradation percentages over one site multi-cores is lower because the computations to communications ratio is decreased. Therefore, selecting bigger frequencies by the scaling algorithm are proportional to this ratio, and thus the execution time do not increase significantly.}