X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/rce2015.git/blobdiff_plain/73774e6e571d69ea4617d9b1998609410f4f2520..5319c6448989a67e062c412732f84404b3195ae0:/paper.tex diff --git a/paper.tex b/paper.tex index 5fcebfb..094f1aa 100644 --- a/paper.tex +++ b/paper.tex @@ -94,31 +94,26 @@ Email:~\email{l.zianekhodja@ulg.ac.be} } -\begin{abstract} The behavior of multi-core applications is always a challenge -to predict, especially with a new architecture for which no experiment has been -performed. With some applications, it is difficult, if not impossible, to build -accurate performance models. That is why another solution is to use a simulation -tool which allows us to change many parameters of the architecture (network -bandwidth, latency, number of processors) and to simulate the execution of such -applications. The main contribution of this paper is to show that the use of a -simulation tool (here we have decided to use the SimGrid toolkit) can really -help developers to better tune their applications for a given multi-core -architecture. - -%In particular we focus our attention on two parallel iterative algorithms based -%on the Multisplitting algorithm and we compare them to the GMRES algorithm. -%These algorithms are used to solve linear systems. Two different variants of -%the Multisplitting are studied: one using synchronoous iterations and another -%one with asynchronous iterations. -In this paper we focus our attention on the simulation of iterative algorithms to solve sparse linear systems on large clusters. We study the behavior of the widely used GMRES algorithm and two different variants of the Multisplitting algorithms: one using synchronous iterations and another one with asynchronous iterations. -For each algorithm we have simulated -different architecture parameters to evaluate their influence on the overall -execution time. -%The obtain simulated results confirm the real results -%previously obtained on different real multi-core architectures and also confirm -%the efficiency of the asynchronous Multisplitting algorithm compared to the -%synchronous GMRES method. -The simulations confirm the real results previously obtained on different real multi-core architectures and also confirm the efficiency of the asynchronous Multisplitting algorithm on distant clusters compared to the synchronous GMRES algorithm. +\begin{abstract} %% The behavior of multi-core applications is always a challenge +%% to predict, especially with a new architecture for which no experiment has been +%% performed. With some applications, it is difficult, if not impossible, to build +%% accurate performance models. That is why another solution is to use a simulation +%% tool which allows us to change many parameters of the architecture (network +%% bandwidth, latency, number of processors) and to simulate the execution of such +%% applications. The main contribution of this paper is to show that the use of a +%% simulation tool (here we have decided to use the SimGrid toolkit) can really +%% help developers to better tune their applications for a given multi-core +%% architecture. + +%% In this paper we focus our attention on the simulation of iterative algorithms to solve sparse linear systems on large clusters. We study the behavior of the widely used GMRES algorithm and two different variants of the Multisplitting algorithms: one using synchronous iterations and another one with asynchronous iterations. +%% For each algorithm we have simulated +%% different architecture parameters to evaluate their influence on the overall +%% execution time. +%% The simulations confirm the real results previously obtained on different real multi-core architectures and also confirm the efficiency of the asynchronous Multisplitting algorithm on distant clusters compared to the synchronous GMRES algorithm. + +The behavior of multi-core applications is always a challenge to predict, especially with a new architecture for which no experiment has been performed. With some applications, it is difficult, if not impossible, to build accurate performance models. That is why another solution is to use a simulation tool which allows us to change many parameters of the architecture (network bandwidth, latency, number of processors) and to simulate the execution of such applications. + +In this paper we focus on the simulation of iterative algorithms to solve sparse linear systems. We study the behavior of the GMRES algorithm and two different variants of the multisplitting algorithms: using synchronous or asynchronous iterations. For each algorithm we have simulated different architecture parameters to evaluate their influence on the overall execution time. The simulations confirm the real results previously obtained on different real multi-core architectures and also confirm the efficiency of the asynchronous multisplitting algorithm on distant clusters compared to the GMRES algorithm. \end{abstract} @@ -154,10 +149,10 @@ task cannot begin a new iteration while it has not received data dependencies from its neighbors. We say that the iteration computation follows a \textit{synchronous} scheme. In the asynchronous scheme a task can compute a new iteration without having to wait for the data dependencies coming from its -neighbors. Both communication and computations are \textit{asynchronous} +neighbors. Both communications and computations are \textit{asynchronous} inducing that there is no more idle time, due to synchronizations, between two iterations~\cite{bcvc06:ij}. This model presents some advantages and drawbacks -that we detail in section~\ref{sec:asynchro} but even if the number of +that we detail in Section~\ref{sec:asynchro} but even if the number of iterations required to converge is generally greater than for the synchronous case, it appears that the asynchronous iterative scheme can significantly reduce overall execution times by suppressing idle times due to @@ -170,7 +165,7 @@ allocations policies under varying CPU power, network speeds and loads is very challenging and labor intensive~\cite{Calheiros:2011:CTM:1951445.1951450}. This problematic is even more difficult for the asynchronous scheme where a small parameter variation of the execution platform and of the application data can -lead to very different numbers of iterations to reach the converge and so to +lead to very different numbers of iterations to reach the convergence and so to very different execution times. In this challenging context we think that the use of a simulation tool can greatly leverage the possibility of testing various platform scenarios. @@ -178,16 +173,16 @@ platform scenarios. The {\bf main contribution of this paper} is to show that the use of a simulation tool (i.e. the SimGrid toolkit~\cite{SimGrid}) in the context of real parallel applications (i.e. large linear system solvers) can help developers to -better tune their application for a given multi-core architecture. To show the +better tune their applications for a given multi-core architecture. To show the validity of this approach we first compare the simulated execution of the Krylov -multisplitting algorithm with the GMRES (Generalized Minimal Residual) +multisplitting algorithm with the GMRES (Generalized Minimal RESidual) solver~\cite{saad86} in synchronous mode. The simulation results allow us to -determine which method to choose given a specified multi-core architecture. +determine which method to choose for a given multi-core architecture. Moreover the obtained results on different simulated multi-core architectures confirm the real results previously obtained on non simulated architectures. More precisely the simulated results are in accordance (i.e. with the same order of magnitude) with the works presented in~\cite{couturier15}, which show that -the synchronous multisplitting method is more efficient than GMRES for large +the synchronous Krylov multisplitting method is more efficient than GMRES for large scale clusters. Simulated results also confirm the efficiency of the asynchronous multisplitting algorithm compared to the synchronous GMRES especially in case of geographically distant clusters. @@ -200,9 +195,9 @@ asynchronous iterative application. This paper is organized as follows. Section~\ref{sec:asynchro} presents the iteration model we use and more particularly the asynchronous scheme. In -section~\ref{sec:simgrid} the SimGrid simulation toolkit is presented. +Section~\ref{sec:simgrid} the SimGrid simulation toolkit is presented. Section~\ref{sec:04} details the different solvers that we use. Finally our -experimental results are presented in section~\ref{sec:expe} followed by some +experimental results are presented in Section~\ref{sec:expe} followed by some concluding remarks and perspectives. @@ -568,8 +563,8 @@ architectures and scaling up the input matrix size} \hline Grid Architecture & 2x16, 4x8, 4x16 and 8x8\\ %\hline Network & N2 : bw=1Gbits/s - lat=5.10$^{-5}$ \\ %\hline - Input matrix size & N$_{x}$ x N$_{y}$ x N$_{z}$ =150 x 150 x 150\\ %\hline - - & N$_{x}$ x N$_{y}$ x N$_{z}$ =170 x 170 x 170 \\ \hline + Input matrix size & N$_{x}$ $\times$ N$_{y}$ $\times$ N$_{z}$ =150 $\times$ 150 $\times$ 150\\ %\hline + - & N$_{x}$ $\times$ N$_{y}$ $\times$ N$_{z}$ =170 $\times$ 170 $\times$ 170 \\ \hline \end{tabular} \caption{Test conditions: various grid configurations with the input matix size N$_{x}$=150 or N$_{x}$=170 \RC{N2 n'est pas défini..}\RC{Nx est défini, Ny? Nz?} \AG{La lettre 'x' n'est pas le symbole de la multiplication. Utiliser \texttt{\textbackslash times}. Idem dans le texte, les figures, etc.}} @@ -595,7 +590,7 @@ multisplitting method. \begin{center} \includegraphics[width=100mm]{cluster_x_nodes_nx_150_and_nx_170.pdf} \end{center} - \caption{Various grid configurations with the input matrix size N$_{x}$=150 and N$_{x}$=170\RC{idem} + \caption{Various grid configurations with the input matrix size $N_{x}=150$ and $N_{x}=170$\RC{idem} \AG{Utiliser le point comme séparateur décimal et non la virgule. Idem dans les autres figures.}} \label{fig:01} \end{figure} @@ -617,7 +612,7 @@ $40\%$ better (resp. $48\%$) when running from 2x16=32 to 8x8=64 processors. \RC Grid Architecture & 2x16, 4x8\\ %\hline Network & N1 : bw=10Gbs-lat=8.10$^{-6}$ \\ %\hline - & N2 : bw=1Gbs-lat=5.10$^{-5}$ \\ - Input matrix size & N$_{x}$ x N$_{y}$ x N$_{z}$ =150 x 150 x 150\\ \hline + Input matrix size & $N_{x} \times N_{y} \times N_{z} =150 \times 150 \times 150$\\ \hline \end{tabular} \caption{Test conditions: grid 2x16 and 4x8 with networks N1 vs N2} \label{tab:02} @@ -651,7 +646,7 @@ the network speed drops down (variation of 12.5\%), the difference between t \hline Grid Architecture & 2x16\\ %\hline Network & N1 : bw=1Gbs \\ %\hline - Input matrix size & N$_{x}$ x N$_{y}$ x N$_{z}$ =150 x 150 x 150\\ \hline + Input matrix size & $N_{x} \times N_{y} \times N_{z} = 150 \times 150 \times 150$\\ \hline \end{tabular} \caption{Test conditions: network latency impacts} \label{tab:03} @@ -687,7 +682,7 @@ magnitude with a latency of $8.10^{-6}$. \hline Grid Architecture & 2x16\\ %\hline Network & N1 : bw=1Gbs - lat=5.10$^{-5}$ \\ %\hline - Input matrix size & N$_{x}$ x N$_{y}$ x N$_{z}$ =150 x 150 x 150\\ \hline \\ + Input matrix size & $N_{x} \times N_{y} \times N_{z} =150 \times 150 \times 150$\\ \hline \\ \end{tabular} \caption{Test conditions: Network bandwidth impacts\RC{Qu'est ce qui varie ici? Il n'y a pas de variation dans le tableau}} \label{tab:04} @@ -716,7 +711,7 @@ of $40\%$ which is only around $24\%$ for the classical GMRES. \hline Grid Architecture & 4x8\\ %\hline Network & N2 : bw=1Gbs - lat=5.10$^{-5}$ \\ - Input matrix size & N$_{x}$ = From 40 to 200\\ \hline + Input matrix size & $N_{x}$ = From 40 to 200\\ \hline \end{tabular} \caption{Test conditions: Input matrix size impacts} \label{tab:05} @@ -756,7 +751,7 @@ grid 2x16 leading to the same conclusion. \hline Grid architecture & 2x16\\ %\hline Network & N2 : bw=1Gbs - lat=5.10$^{-5}$ \\ %\hline - Input matrix size & N$_{x}$ = 150 x 150 x 150\\ \hline + Input matrix size & $N_{x} = 150 \times 150 \times 150$\\ \hline \end{tabular} \caption{Test conditions: CPU Power impacts} \label{tab:06} @@ -819,7 +814,7 @@ The test conditions are summarized in the table~\ref{tab:07}: \\ Processors Power & 1 GFlops to 1.5 GFlops\\ Intra-Network & bw=1.25 Gbits - lat=5.10$^{-5}$ \\ %\hline Inter-Network & bw=5 Mbits - lat=2.10$^{-2}$\\ - Input matrix size & N$_{x}$ = From 62 to 150\\ %\hline + Input matrix size & $N_{x}$ = From 62 to 150\\ %\hline Residual error precision & 10$^{-5}$ to 10$^{-9}$\\ \hline \\ \end{tabular} \caption{Test conditions: GMRES in synchronous mode vs Krylov Multisplitting in asynchronous mode} @@ -830,7 +825,7 @@ Again, comprehensive and extensive tests have been conducted with different parameters as the CPU power, the network parameters (bandwidth and latency) and with different problem size. The relative gains greater than $1$ between the two algorithms have been captured after each step of the test. In -Figure~\ref{fig:07} are reported the best grid configurations allowing +Table~\ref{tab:08} are reported the best grid configurations allowing the multisplitting method to be more than $2.5$ times faster than the classical GMRES. These experiments also show the relative tolerance of the multisplitting algorithm when using a low speed network as usually observed with @@ -845,7 +840,7 @@ geographically distant clusters through the internet. \end{tabular}} -\begin{figure}[!t] +\begin{table}[!t] \centering %\begin{table} % \caption{Relative gain of the multisplitting algorithm compared with the classical GMRES} @@ -872,14 +867,39 @@ geographically distant clusters through the internet. \hline \end{mytable} %\end{table} - \caption{Relative gain of the multisplitting algorithm compared with the classical GMRES -\AG{C'est un tableau, pas une figure}} - \label{fig:07} -\end{figure} + \caption{Relative gain of the multisplitting algorithm compared with the classical GMRES} + \label{tab:08} +\end{table} \section{Conclusion} -CONCLUSION + +In this paper we have presented the simulation of the execution of three +different parallel solvers on some multi-core architectures. We have show that +the SimGrid toolkit is an interesting simulation tool that has allowed us to +determine which method to choose given a specified multi-core architecture. +Moreover the simulated results are in accordance (i.e. with the same order of +magnitude) with the works presented in~\cite{couturier15}. Simulated results +also confirm the efficiency of the asynchronous multisplitting +algorithm compared to the synchronous GMRES especially in case of +geographically distant clusters. + +These results are important since it is very time consuming to find optimal +configuration and deployment requirements for a given application on a given +multi-core architecture. Finding good resource allocations policies under +varying CPU power, network speeds and loads is very challenging and labor +intensive. This problematic is even more difficult for the asynchronous +scheme where a small parameter variation of the execution platform and of the +application data can lead to very different numbers of iterations to reach the +converge and so to very different execution times. + + +In future works, we plan to investigate how to simulate the behavior of really +large scale applications. For example, if we are interested to simulate the +execution of the solvers of this paper with thousand or even dozens of thousands +or core, it is not possible to do that with SimGrid. In fact, this tool will +make the real computation. So we plan to focus our research on that problematic. + %\section*{Acknowledgment}