Corrections coquilles abstruct et intro

[rce2015.git] / paper.tex

diff --git a/paper.tex b/paper.tex
index e6bf7662468deca95e89a7173c3c595c91a1e585..094f1aa7c26ade55d838a3d675f748498fadb3c4 100644 (file)
--- a/paper.tex
+++ b/paper.tex
@@ -94,31 +94,26 @@
   Email:~\email{l.zianekhodja@ulg.ac.be}
 }
 
   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}
 
 
 \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
 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
 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
 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
 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.
 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
 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
 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
 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
 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.
 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
 
 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
 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.
 
 
 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
  \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.}}
  \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}
   \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}
 \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$^{-5}$ \\
  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}
  \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
  \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}
  \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
  \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}
  \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$^{-5}$ \\
  \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}
  \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
  \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}
  \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$^{-2}$\\
  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}
  Residual error precision & 10$^{-5}$ to 10$^{-9}$\\ \hline \\
  \end{tabular}
 \caption{Test conditions: GMRES in synchronous mode vs Krylov Multisplitting in asynchronous mode}


@@ -899,7 +894,11 @@ application data can lead to very different numbers of iterations to reach the
 converge and so to very different execution times.
 
 
 converge and so to very different execution times.
 
 

-Our future works...
+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.