X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/Krylov_multi.git/blobdiff_plain/fd654bb9a37efb3530aed230af886466bbb7f42a..a14f810ba9ed3a59ffb2913d9a502b312767dac6:/krylov_multi.tex?ds=inline

diff --git a/krylov_multi.tex b/krylov_multi.tex
index 92730ac..1ab94c6 100644
--- a/krylov_multi.tex
+++ b/krylov_multi.tex
@@ -6,6 +6,7 @@
 \usepackage{algorithm}
 \usepackage{algpseudocode}
 \usepackage{multirow}
+\usepackage{authblk}
 
 \algnewcommand\algorithmicinput{\textbf{Input:}}
 \algnewcommand\Input{\item[\algorithmicinput]}
@@ -17,18 +18,23 @@
 \newcommand{\Prec}{\mathit{prec}}
 \newcommand{\Ratio}{\mathit{Ratio}}
 
-%\usepackage{xspace}
-%\usepackage[textsize=footnotesize]{todonotes}
-%\newcommand{\LZK}[2][inline]{%
-%\todo[color=green!40,#1]{\sffamily\textbf{LZK:} #2}\xspace}
+\def\changemargin#1#2{\list{}{\rightmargin#2\leftmargin#1}\item[]}
+\let\endchangemargin=\endlist
 
-\title{A scalable multisplitting algorithm for solving large sparse linear systems} 
+\title{A scalable multisplitting algorithm to solve large sparse linear systems} 
 \date{}
 
-
-
+\author[1]{RaphaÃ«l Couturier}
+\author[2]{ Lilia Ziane Khodja}
+\affil[1]{ Femto-ST Institute\\
+    University of Franche Comte\\
+    France\\
+    email: raphael.couturier@univ-fcomte.fr}
+\affil[2]{Inria Bordeaux Sud-Ouest\\
+    France\\
+    email: lilia.ziane@inria.fr}
 \begin{document}
-\author{RaphaÃ«l Couturier \and Lilia Ziane Khodja}
+
 
 \maketitle
 
@@ -45,7 +51,7 @@ parallel GMRES method inside each block and on a parallel Krylov minimization in
 order to improve the convergence. Some large scale experiments with a 3D Poisson
 problem  are  presented  with  up   to  8,192  cores.   They  show  the  obtained
 improvements compared to a classical GMRES both in terms of number of iterations
-and execution times.
+and in terms of execution times.
 \end{abstract}
 
 %%%%%%%%%%%%%%%%%%%%%%%%
@@ -54,9 +60,9 @@ and execution times.
 \section{Introduction}
 Iterative methods are used to solve  large sparse linear systems of equations of
 the form  $Ax=b$ because they are  easier to parallelize than  direct ones. Many
-iterative  methods have  been proposed  and  adapted by  many researchers.   For
+iterative  methods have  been proposed  and  adapted by  different researchers.   For
 example, the GMRES method and the  Conjugate Gradient method are very well known
-and  used by  many researchers~\cite{S96}. Both methods  are based  on the
+and  used~\cite{S96}. Both methods  are based  on the
 Krylov subspace which consists in forming  a basis of a sequence of successive
 matrix powers times the initial residual.
 
@@ -67,46 +73,68 @@ Preconditioners can be  used in order to increase  the convergence of iterative
 solvers.   However, most  of the  good preconditioners  are not  scalable when
 thousands of cores are used.
 
-Traditional iterative  solvers have  global synchronizations that  penalize the
-scalability.   Two  possible solutions  consists  either  in using  asynchronous
-iterative  methods~\cite{ref18} or  to  use multisplitting  algorithms. In  this
-paper, we will  reconsider the use of a multisplitting  method. In opposition to
-traditional  multisplitting  method  that  suffer  from  slow  convergence,  as
-proposed  in~\cite{huang1993krylov},  the  use  of a  minimization  process  can
-drastically improve the convergence.
+%Traditional iterative  solvers have  global synchronizations that  penalize the
+%scalability.   Two  possible solutions  consists  either  in using  asynchronous
+%iterative  methods~\cite{ref18} or  to  use multisplitting  algorithms. In  this
+%paper, we will  reconsider the use of a multisplitting  method. In opposition to
+%traditional  multisplitting  method  that  suffer  from  slow  convergence,  as
+%proposed  in~\cite{huang1993krylov},  the  use  of a  minimization  process  can
+%drastically improve the convergence.
+
+Traditional parallel iterative solvers are based on fine-grain computations that
+frequently  require  data exchanges  between  computing  nodes  and have  global
+synchronizations  that penalize  the  scalability. Particularly,  they are  more
+penalized on large  scale architectures or on distributed  platforms composed of
+distant  clusters interconnected  by  a high-latency  network.  It is  therefore
+imperative to develop coarse-grain based algorithms to reduce the communications
+in the  parallel iterative  solvers. Two possible  solutions consists  either in
+using  asynchronous  iterative  methods~\cite{ref18}  or in  using  multisplitting
+algorithmss.  In this  paper,  we will  reconsider  the use  of a  multisplitting
+method. In opposition to traditional multisplitting method that suffer from slow
+convergence, as  proposed in~\cite{huang1993krylov},  the use of  a minimization
+process can drastically improve the convergence.
+
+The present paper is  organized as follows. First, Section~\ref{sec:02} presents
+some  related  works and  the  principle  of  multisplitting methods.  Then,  in
+Section~\ref{sec:03}  the algorithm  of our  Krylov multisplitting
+method, based  on inner-outer  iterations, is presented. Finally, in  Section~\ref{sec:04}, the
+parallel experiments on Hector architecture  show the performances of the Krylov
+multisplitting algorithm compared to the classical GMRES algorithm to solve a 3D
+Poisson problem.
 
 
 %%%%%%%%%%%%%%%%%%%%%%%%
 %%%%%%%%%%%%%%%%%%%%%%%%
 
-\section{Related works and presention of the multisplitting method}
-A general framework  for studying parallel multisplitting has  been presented in~\cite{o1985multi}
+\section{Related works and presentation of the multisplitting method}
+\label{sec:02}
+A general framework  to study parallel multisplitting methods has  been presented in~\cite{o1985multi}
 by O'Leary and White. Convergence conditions are given for the
-most general case.  Many authors improved multisplitting algorithms by proposing
-for  example  an  asynchronous  version~\cite{bru1995parallel}  and  convergence
-conditions~\cite{bai1999block,bahi2000asynchronous}   in  this  case   or  other
+most general cases.  Many authors have improved multisplitting algorithms by proposing,
+for  example,  an  asynchronous  version~\cite{bru1995parallel} or  convergence
+conditions~\cite{bai1999block,bahi2000asynchronous}     or  other
 two-stage algorithms~\cite{frommer1992h,bru1995parallel}.
 
-In~\cite{huang1993krylov},  the  authors  proposed  a  parallel  multisplitting
+In~\cite{huang1993krylov},  the  authors  have proposed  a  parallel  multisplitting
 algorithm in which all the tasks except  one are devoted to solve a sub-block of
 the splitting  and to send their  local solutions to  the first task which  is in
-charge to  combine the vectors at  each iteration.  These vectors  form a Krylov
+charge of  combining the vectors at  each iteration.  These vectors  form a Krylov
 basis for  which the first task minimizes  the error function over  the basis to
-increase the convergence, then the other tasks receive the updated solution until
+increase the convergence, then the other tasks receive the updated solution until the
 convergence of the global system. 
 
-In~\cite{couturier2008gremlins}, the  authors proposed practical implementations
+In~\cite{couturier2008gremlins}, the  authors have developed practical implementations
 of multisplitting algorithms to solve  large scale linear systems. Inner solvers
 could be  based on sequential direct method  with the LU method  or sequential iterative
 one with GMRES.
 
-In~\cite{prace-multi},  the  authors have  proposed a  parallel  multisplitting
+In~\cite{prace-multi},  the  authors have  designed a  parallel  multisplitting
 algorithm in which large blocks are solved using a GMRES solver. The authors have
 performed large scale experiments up-to  32,768 cores and they conclude that
-asynchronous  multisplitting algorithm  could be more  efficient  than traditional
+an asynchronous  multisplitting algorithm  could be more  efficient  than traditional
 solvers on an exascale architecture with hundreds of thousands of cores.
 
-So compared to these works, we propose in this paper a practical multisplitting method based on parallel iterative blocks and gives better results than classical GMRES method for the 3D Poisson problem we considered.
+So, compared to these works, we propose in this paper a practical multisplitting method based on parallel iterative blocks which gives better results than classical GMRES method for the 3D Poisson problem we considered.
 \\
 
 The key idea of a multisplitting method to solve a large system of linear equations $Ax=b$ is defined as follows. The first step consists in partitioning the matrix $A$ in $L$ several ways 
@@ -114,7 +142,7 @@ The key idea of a multisplitting method to solve a large system of linear equati
 A = M_\ell - N_\ell,
 \label{eq01}
 \end{equation}
-where for all $\ell\in\{1,\ldots,L\}$ $M_\ell$ are non-singular matrices. Then the linear system is solved by iteration based on the obtained splittings as follows
+where for all $\ell\in\{1,\ldots,L\}$ $M_\ell$ are non-singular matrices. Then the linear system is solved by an iteration based on the obtained splittings as follows
 \begin{equation}
 x^{k+1}=\displaystyle\sum^L_{\ell=1} E_\ell M^{-1}_\ell (N_\ell x^k + b),~k=1,2,3,\ldots
 \label{eq02}
@@ -142,6 +170,7 @@ In the case where the diagonal weighting matrices $E_\ell$ have only zero and on
 %%%%%%%%%%%%%%%%%%%%%%%%
 
 \section{A two-stage method with a minimization}
+\label{sec:03}
 Let $Ax=b$ be a given large and sparse linear system of $n$ equations to solve in parallel on $L$ clusters of processors, physically adjacent or geographically distant, where $A\in\mathbb{R}^{n\times n}$ is a square and non-singular matrix, $x\in\mathbb{R}^{n}$ is the solution vector and $b\in\mathbb{R}^{n}$ is the right-hand side vector. The multisplitting of this linear system is defined as follows
 \begin{equation}
 \left\{
@@ -179,8 +208,8 @@ of       the       global      sparse       linear       system      to       be
 solved~\cite{o1985multi,ref18}. Furthermore, the  splitting of the linear system
 among  several clusters  of  processors  increases the  spectral  radius of  the
 iteration  matrix, thereby  slowing the  convergence.  In  fact, the  larger the
-number of  splitting is, the larger the  spectral radius is.  In  this paper, we
-based  on   the  work   presented  in~\cite{huang1993krylov}  to   increase  the
+number of  splitting is, the larger the  spectral radius is.  In  this paper, our
+work is based  on   the  work   presented  in~\cite{huang1993krylov}  to   increase  the
 convergence and improve the scalability of the multisplitting methods.
 
 In order to accelerate the convergence, we implemented the outer iteration of the multisplitting solver as a Krylov iterative method which minimizes some error function over a Krylov subspace~\cite{S96}. The Krylov subspace that we used is spanned by a basis composed of successive solutions issued from solving the $L$ splittings~(\ref{sec03:eq03})
@@ -188,7 +217,7 @@ In order to accelerate the convergence, we implemented the outer iteration of th
 S=\{x^1,x^2,\ldots,x^s\},~s\leq n,
 \label{sec03:eq04}
 \end{equation}
-where for $j\in\{1,\ldots,s\}$, $x^j=[X_1^j,\ldots,X_L^j]$ is a solution of the global linear system. The advantage of such a Krylov subspace is that we need neither an orthogonal basis nor synchronizations between clusters to generate this basis.
+where for $j\in\{1,\ldots,s\}$, $x^j=[X_1^j,\ldots,X_L^j]$ is a solution of the global linear system. The advantage of such a Krylov subspace is that we neither need an orthogonal basis nor any synchronization between clusters to generate this basis.
 
 The multisplitting method is periodically restarted every $s$ iterations with a new initial guess $\tilde{x}=S\alpha$ which minimizes the error function $\|b-Ax\|_2$ over the Krylov subspace spanned by vectors of $S$. So $\alpha$ is defined as the solution of the large overdetermined linear system
 \begin{equation}
@@ -247,14 +276,15 @@ where $R^T$ denotes the transpose of matrix $R$. Since $R$ (i.e. $AS$) and $b$ a
 \label{algo:01}
 \end{algorithm}
 
-The main key points of our Krylov multisplitting method to solve a large sparse linear system are given in Algorithm~\ref{algo:01}. This algorithm is based on a two-stage method with a minimization using restarted GMRES iterative method as an inner solver. It is executed in parallel by each cluster of processors. Matrices and vectors with the subscript $\ell$ represent the local data for cluster $\ell$, where $\ell\in\{1,\ldots,L\}$. The two-stage solver uses two different parallel iterative algorithms: GMRES method to solve each splitting~(\ref{sec03:eq03}) on a cluster of processors, and CGNR method executed in parallel by all clusters to minimize the function error~(\ref{sec03:eq07}) over the Krylov subspace spanned by $S$. The algorithm requires two global synchronizations between $L$ clusters. The first one is performed at line~\ref{line12} in Algorithm~\ref{algo:01} to exchange local values of vector solution $x$ (i.e. the minimizer $\tilde{x}$) required to restart the multisplitting solver. The second one is needed to construct the matrix $R$. We chose to perform this latter synchronization $s$ times in every outer iteration $k$ (line~\ref{line7} in Algorithm~\ref{algo:01}). This is a straightforward way to compute the sparse matrix-dense matrix multiplication $R=AS$. We implemented all synchronizations by using message passing collective communications of MPI library.
+The main key points of our Krylov multisplitting method to solve a large sparse linear system are given in Algorithm~\ref{algo:01}. This algorithm is based on a two-stage method with a minimization using restarted GMRES iterative method as an inner solver. It is executed in parallel by each cluster of processors. Matrices and vectors with the subscript $\ell$ represent the local data for cluster $\ell$, where $\ell\in\{1,\ldots,L\}$. The two-stage solver uses two different parallel iterative algorithms: the GMRES method to solve each splitting~(\ref{sec03:eq03}) on a cluster of processors, and the CGNR method, executed in parallel by all clusters, to minimize the function error~(\ref{sec03:eq07}) over the Krylov subspace spanned by $S$. The algorithm requires two global synchronizations between $L$ clusters. The first one is performed  line~\ref{line12} in Algorithm~\ref{algo:01} to exchange local values of vector solution $x$ (i.e. the minimizer $\tilde{x}$) required to restart the multisplitting solver. The second one is needed to construct the matrix $R$. We chose to perform this latter synchronization $s$ times in every outer iteration $k$ (line~\ref{line7} in Algorithm~\ref{algo:01}). This is a straightforward way to compute the sparse matrix-dense matrix multiplication $R=AS$. We implemented all synchronizations by using message passing collective communications of MPI library.
 
 %%%%%%%%%%%%%%%%%%%%%%%%
 %%%%%%%%%%%%%%%%%%%%%%%%
 
 \section{Experiments}
-In order to illustrate  the interest  of our algorithm. We have  compared our
-algorithm  with  the  GMRES  method  which  is a very  well  used  method  in  many
+\label{sec:04}
+In order to illustrate  the interest  of our algorithm, we have  compared our
+algorithm  with  the  GMRES  method  which  is a commonly  used  method  in  many
 situations.  We have chosen to focus on only one problem which is very simple to
 implement: a 3 dimension Poisson problem.
 
@@ -276,21 +306,21 @@ preconditioner  it  is   possible  to  reduce  the  number   of  iterations  but
 preconditioners are not scalable when using many cores.
 
 %Doing many experiments  with many cores is  not easy and requires to  access to a supercomputer  with several  hours for  developing  a code  and then  improving it. 
-In the following we present some experiments we could achieved out on the Hector
-architecture, the  previous UK's high-end  computing resource, funded by  the UK
-Research Councils. This  is a Cray XE6 supercomputer,  equipped with two 16-core
-AMD Opteron 2.3 Ghz  and 32 GB of memory. Machines are  interconnected with a 3D
-torus.
+In the following we present some experiments we could achieve out on the Hector
+architecture,  a UK's  high-end computing  resource, funded  by the  UK Research
+Councils~\cite{hector}.  This is  a Cray  XE6 supercomputer,  equipped  with two
+16-core AMD  Opteron 2.3 Ghz  and 32 GB  of memory. Machines  are interconnected
+with a 3D torus.
 
 Table~\ref{tab1} shows  the result of  the experiments.  The first  column shows
 the  size of  the  3D Poisson  problem.  The size  is chosen  in  order to  have
 approximately  50,000 components  per core.   The second  column  represents the
-number of  cores used. In parenthesis,  there is the decomposition  used for the
+number of  cores used. In brackets,  one can find the decomposition  used for the
 Krylov multisplitting. The  third column and the sixth  column respectively show
 the execution time for the GMRES  and the Krylov multisplitting codes. The fourth
-and  the   seventh  column  describes   the  number  of  iterations.    For  the
+and  the   seventh  column  describe   the  number  of  iterations.    For  the
 multisplitting  code, the  total number  of inner  iterations is  represented in
-parenthesis. For  the GMRES code (alone  and in the  multisplitting version) the
+brackets. For  the GMRES code (alone  and in the  multisplitting version) the
 restart parameter is fixed to 16. The precision of the GMRES version is fixed to
 1e-6. For  the multisplitting,  there are two  precisions, one for  the external
 solver which is fixed to 1e-6 and another one for the inner solver (GMRES) which
@@ -302,6 +332,8 @@ is reached. The precision and the maximum number of iterations of CGNR method ar
 
 \begin{table}[htbp]
 \begin{center}
+\begin{changemargin}{-1.8cm}{0cm}
+\begin{small}
 \begin{tabular}{|c|c||c|c|c||c|c|c||c|} 
 \hline
 \multirow{2}{*}{Pb size}&\multirow{2}{*}{Nb. cores} &  \multicolumn{3}{c||}{GMRES} &  \multicolumn{3}{c||}{Krylov Multisplitting} & \multirow{2}{*}{Ratio}\\
@@ -320,16 +352,18 @@ $743^3$ & 8,192 (4x2,048)        & 704.4     & 87,822    & 4.80e-07 &  110.3   &
 \end{tabular}
 \caption{Results}
 \label{tab1}
+\end{small}
+\end{changemargin}
 \end{center}
 \end{table}
 
 
 From these  experiments, it can be  observed that the  multisplitting version is
 always  faster   than  the  GMRES   version.   The  acceleration  gain   of  the
-multisplitting version is between 4 and 6.  It can be noticed that the number of
+multisplitting version ranges between 4 and 6.  It can be noticed that the number of
 iterations is drastically reduced with the multisplitting version even it is not
-neglectable. Moreover, with 8,192 cores, we  can see that using 4 clusters gives
-better performance than simply using 2 clusters. In fact, we can remark that the
+negligible. Moreover, with 8,192 cores, we  can see that using 4 clusters gives a
+better performance than simply using 2 clusters. In fact, we can notice that the
 precision with 2 clusters is slightly  better but in both cases the precision is
 under the specified threshold.
 
@@ -350,22 +384,24 @@ issued from  the discretization of  a 3D Poisson  problem. We have  compared its
 performances to the  classical GMRES method on a  supercomputer composed of 2,048
 to 8,192 cores. The experimental results showed that the multisplitting method is
 about 4  to 6  times faster  than the GMRES  method for  different sizes  of the
-problem split into  2 or 4 blocks when using  multisplitting method. Indeed, the
+problem split into  2 or 4 blocks when using the  multisplitting method. Indeed, the
 GMRES  method  has  difficulties to  scale  with  many  cores while  the  Krylov
 multisplitting  method  allows to  hide  latency  and  reduce the  inter-cluster
 communications.
 
-In future  works, we plan to conduct  experiments on larger number  of cores and
+In future  works, we plan to conduct  experiments on larger numbers  of cores and
 test  the  scalability  of  our   Krylov  multisplitting  method.  It  would  be
 interesting  to validate its  performances to  solve other  linear/nonlinear and
 symmetric/nonsymmetric problems.  Moreover, we intend  to develop multisplitting
-methods based  on asynchronous iteration in which  communications are overlapped
+methods based  on asynchronous iterations in which  communications are overlapped
 by computations.  These methods would  be interesting for platforms  composed of
 distant  clusters interconnected  by  a high-latency  network.  In addition,  we
 intend  to investigate  the  convergence  improvements of  our  method by  using
 preconditioning  techniques  for  Krylov  iterative methods  and  multisplitting
 methods with overlapping blocks.
 
+\section{Acknowledgement}
+The authors would like to thank Mark Bull of the EPCC his fruitful remarks and the facilities of HECToR.
 
 %Other applications (=> other matrices)\\
 %Larger experiments\\
