X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/hpcc2014.git/blobdiff_plain/3ec1ddff4b3b00471eda369b5a9bff6aff4bd8c9..0f68e012ddd8ecc63c7f30090ff7bc057fe66d81:/hpcc.tex

diff --git a/hpcc.tex b/hpcc.tex
index c9aaf0c..d60fd6a 100644
--- a/hpcc.tex
+++ b/hpcc.tex
@@ -50,9 +50,9 @@
 \author{%
   \IEEEauthorblockN{%
     Charles Emile Ramamonjisoa\IEEEauthorrefmark{1},
+    Lilia Ziane Khodja\IEEEauthorrefmark{2},
     David Laiymani\IEEEauthorrefmark{1},
-    Arnaud Giersch\IEEEauthorrefmark{1},
-    Lilia Ziane Khodja\IEEEauthorrefmark{2} and
+    Arnaud Giersch\IEEEauthorrefmark{1} and
     RaphaÃ«l Couturier\IEEEauthorrefmark{1}
   }
   \IEEEauthorblockA{\IEEEauthorrefmark{1}%
@@ -71,34 +71,20 @@
 
 \maketitle
 
-\RC{Ordre des auteurs pas dÃ©finitif.}
 \begin{abstract}
-\AG{L'abstract est AMHA incomprÃ©hensible et ne donne pas envie de lire la suite.}
-In recent years, the scalability of large-scale implementation in a 
-distributed environment of algorithms becoming more and more complex has 
-always been hampered by the limits of physical computing resources 
-capacity. One solution is to run the program in a virtual environment 
-simulating a real interconnected computers architecture. The results are 
-convincing and useful solutions are obtained with far fewer resources 
-than in a real platform. However, challenges remain for the convergence 
-and efficiency of a class of algorithms that concern us here, namely 
-numerical parallel iterative algorithms executed in asynchronous mode, 
-especially in a large scale level. Actually, such algorithm requires a 
-balance and a compromise between computation and communication time 
-during the execution. Two important factors determine the success of the 
-experimentation: the convergence of the iterative algorithm on a large 
-scale and the execution time reduction in asynchronous mode. Once again, 
-from the current work, a simulated environment like SimGrid provides
-accurate results which are difficult or even impossible to obtain in a 
-physical platform by exploiting the flexibility of the simulator on the 
-computing units clusters and the network structure design. Our 
-experimental outputs showed a saving of up to \np[\%]{40} for the algorithm
-execution time in asynchronous mode compared to the synchronous one with 
-a residual precision up to \np{E-11}. Such successful results open
-perspectives on experimentations for running the algorithm on a 
-simulated large scale growing environment and with larger problem size. 
-
-\LZK{Long\ldots}
+
+Synchronous  iterative  algorithms  is  often less  scalable  than  asynchronous
+iterative  ones.  Performing  large  scale experiments  with  different kind  of
+networks parameters is not easy  because with supercomputers such parameters are
+fixed. So one  solution consists in using simulations first  in order to analyze
+what parameters  could influence or not  the behaviors of an  algorithm. In this
+paper, we show  that it is interesting to use SimGrid  to simulate the behaviors
+of asynchronous  iterative algorithms. For that,  we compare the  behaviour of a
+synchronous  GMRES  algorithm  with  an  asynchronous  multisplitting  one  with
+simulations  in  which we  choose  some parameters.   Both  codes  are real  MPI
+codes. Experiments allow us to see when the multisplitting algorithm can be more
+efficience than the GMRES one to solve a 3D Poisson problem.
+
 
 % no keywords for IEEE conferences
 % Keywords: Algorithm distributed iterative asynchronous simulation SimGrid
@@ -296,51 +282,75 @@ Let $Ax=b$ be a large sparse system of $n$ linear equations in $\mathbb{R}$, whe
       B_L
     \end{array} \right)
 \end{equation*}
-in such a way that successive rows of matrix $A$ and both vectors $x$ and $b$ are assigned to one cluster, where for all $l,m\in\{1,\ldots,L\}$ $A_{lm}$ is a rectangular block of $A$ of size $n_l\times n_m$, $X_l$ and $B_l$ are sub-vectors of $x$ and $b$, respectively, of size $n_l$ each and $\sum_{l} n_l=\sum_{m} n_m=n$.
+in such a way that successive rows of matrix $A$ and both vectors $x$ and $b$
+are assigned to one cluster, where for all $\ell,m\in\{1,\ldots,L\}$, $A_{\ell
+  m}$ is a rectangular block of $A$ of size $n_\ell\times n_m$, $X_\ell$ and
+$B_\ell$ are sub-vectors of $x$ and $b$, respectively, of size $n_\ell$ each,
+and $\sum_{\ell} n_\ell=\sum_{m} n_m=n$.
 
 The multisplitting method proceeds by iteration to solve in parallel the linear system on $L$ clusters of processors, in such a way each sub-system
 \begin{equation}
   \label{eq:4.1}
   \left\{
     \begin{array}{l}
-      A_{ll}X_l = Y_l \text{, such that}\\
-      Y_l = B_l - \displaystyle\sum_{\substack{m=1\\ m\neq l}}^{L}A_{lm}X_m
+      A_{\ell\ell}X_\ell = Y_\ell \text{, such that}\\
+      Y_\ell = B_\ell - \displaystyle\sum_{\substack{m=1\\ m\neq \ell}}^{L}A_{\ell m}X_m
     \end{array}
   \right.
 \end{equation}
-is solved independently by a cluster and communications are required to update the right-hand side sub-vector $Y_l$, such that the sub-vectors $X_m$ represent the data dependencies between the clusters. As each sub-system (\ref{eq:4.1}) is solved in parallel by a cluster of processors, our multisplitting method uses an iterative method as an inner solver which is easier to parallelize and more scalable than a direct method. In this work, we use the parallel algorithm of GMRES method~\cite{ref1} which is one of the most used iterative method by many researchers. 
+is solved independently by a cluster and communications are required to update
+the right-hand side sub-vector $Y_\ell$, such that the sub-vectors $X_m$
+represent the data dependencies between the clusters. As each sub-system
+(\ref{eq:4.1}) is solved in parallel by a cluster of processors, our
+multisplitting method uses an iterative method as an inner solver which is
+easier to parallelize and more scalable than a direct method. In this work, we
+use the parallel algorithm of GMRES method~\cite{ref1} which is one of the most
+used iterative method by many researchers.
 
 \begin{figure}[!t]
   %%% IEEE instructions forbid to use an algorithm environment here, use figure
   %%% instead
 \begin{algorithmic}[1]
-\Input $A_l$ (sparse sub-matrix), $B_l$ (right-hand side sub-vector)
-\Output $X_l$ (solution sub-vector)\vspace{0.2cm}
-\State Load $A_l$, $B_l$
+\Input $A_\ell$ (sparse sub-matrix), $B_\ell$ (right-hand side sub-vector)
+\Output $X_\ell$ (solution sub-vector)\medskip
+
+\State Load $A_\ell$, $B_\ell$
 \State Set the initial guess $x^0$
 \For {$k=0,1,2,\ldots$ until the global convergence}
 \State Restart outer iteration with $x^0=x^k$
 \State Inner iteration: \Call{InnerSolver}{$x^0$, $k+1$}
-\State\label{algo:01:send} Send shared elements of $X_l^{k+1}$ to neighboring clusters
-\State\label{algo:01:recv} Receive shared elements in $\{X_m^{k+1}\}_{m\neq l}$
+\State\label{algo:01:send} Send shared elements of $X_\ell^{k+1}$ to neighboring clusters
+\State\label{algo:01:recv} Receive shared elements in $\{X_m^{k+1}\}_{m\neq \ell}$
 \EndFor
 
 \Statex
 
 \Function {InnerSolver}{$x^0$, $k$}
-\State Compute local right-hand side $Y_l$:
+\State Compute local right-hand side $Y_\ell$:
        \begin{equation*}
-         Y_l = B_l - \sum\nolimits^L_{\substack{m=1\\ m\neq l}}A_{lm}X_m^0
+         Y_\ell = B_\ell - \sum\nolimits^L_{\substack{m=1\\ m\neq \ell}}A_{\ell m}X_m^0
        \end{equation*}
-\State Solving sub-system $A_{ll}X_l^k=Y_l$ with the parallel GMRES method
-\State \Return $X_l^k$
+\State Solving sub-system $A_{\ell\ell}X_\ell^k=Y_\ell$ with the parallel GMRES method
+\State \Return $X_\ell^k$
 \EndFunction
 \end{algorithmic}
 \caption{A multisplitting solver with GMRES method}
 \label{algo:01}
 \end{figure}
 
-Algorithm on Figure~\ref{algo:01} shows the main key points of the multisplitting method to solve a large sparse linear system. This algorithm is based on an outer-inner iteration method where the parallel synchronous GMRES method is used to solve the inner iteration. It is executed in parallel by each cluster of processors. For all $l,m\in\{1,\ldots,L\}$, the matrices and vectors with the subscript $l$ represent the local data for cluster $l$, while $\{A_{lm}\}_{m\neq l}$ are off-diagonal matrices of sparse matrix $A$ and $\{X_m\}_{m\neq l}$ contain vector elements of solution $x$ shared with neighboring clusters. At every outer iteration $k$, asynchronous communications are performed between processors of the local cluster and those of distant clusters (lines~\ref{algo:01:send} and~\ref{algo:01:recv} in Figure~\ref{algo:01}). The shared vector elements of the solution $x$ are exchanged by message passing using MPI non-blocking communication routines.
+Algorithm on Figure~\ref{algo:01} shows the main key points of the
+multisplitting method to solve a large sparse linear system. This algorithm is
+based on an outer-inner iteration method where the parallel synchronous GMRES
+method is used to solve the inner iteration. It is executed in parallel by each
+cluster of processors. For all $\ell,m\in\{1,\ldots,L\}$, the matrices and
+vectors with the subscript $\ell$ represent the local data for cluster $\ell$,
+while $\{A_{\ell m}\}_{m\neq \ell}$ are off-diagonal matrices of sparse matrix
+$A$ and $\{X_m\}_{m\neq \ell}$ contain vector elements of solution $x$ shared
+with neighboring clusters. At every outer iteration $k$, asynchronous
+communications are performed between processors of the local cluster and those
+of distant clusters (lines~\ref{algo:01:send} and~\ref{algo:01:recv} in
+Figure~\ref{algo:01}). The shared vector elements of the solution $x$ are
+exchanged by message passing using MPI non-blocking communication routines.
 
 \begin{figure}[!t]
 \centering
@@ -362,14 +372,14 @@ sets the token to \textit{True} if the local convergence is achieved or to
 global convergence is detected when the master of cluster 1 receives from the
 master of cluster $L$ a token set to \textit{True}. In this case, the master of
 cluster 1 broadcasts a stop message to masters of other clusters. In this work,
-the local convergence on each cluster $l$ is detected when the following
+the local convergence on each cluster $\ell$ is detected when the following
 condition is satisfied
 \begin{equation*}
-  (k\leq \MI) \text{ or } (\|X_l^k - X_l^{k+1}\|_{\infty}\leq\epsilon)
+  (k\leq \MI) \text{ or } (\|X_\ell^k - X_\ell^{k+1}\|_{\infty}\leq\epsilon)
 \end{equation*}
 where $\MI$ is the maximum number of outer iterations and $\epsilon$ is the
 tolerance threshold of the error computed between two successive local solution
-$X_l^k$ and $X_l^{k+1}$.
+$X_\ell^k$ and $X_\ell^{k+1}$.
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 We did not encounter major blocking problems when adapting the multisplitting algorithm previously described to a simulation environment like SimGrid unless some code 
@@ -396,25 +406,34 @@ parameters and the program arguments allows us to compare outputs from the code
 study that the results depend on the following parameters:  
 \begin{itemize}
 \item At the network level, we found that the most critical values are the
-  bandwidth (bw) and the network latency (lat).
+  bandwidth and the network latency.
 \item Hosts power (GFlops) can also influence on the results.
 \item Finally, when submitting job batches for execution, the arguments values
-  passed to the program like the maximum number of iterations or the
-  \textit{external} precision are critical. They allow to ensure not only the
-  convergence of the algorithm but also to get the main objective of the
-  experimentation of the simulation in having an execution time in asynchronous
-  less than in synchronous mode. The ratio between the execution time of asynchronous compared to the synchronous mode is defined as the "relative gain". So, our objective running the algorithm in SimGrid is to obtain a relative gain greater than 1.
+  passed to the program like the maximum number of iterations or the external
+  precision are critical. They allow to ensure not only the convergence of the
+  algorithm but also to get the main objective of the experimentation of the
+  simulation in having an execution time in asynchronous less than in
+  synchronous mode. The ratio between the execution time of asynchronous
+  compared to the synchronous mode is defined as the \emph{relative gain}. So,
+  our objective running the algorithm in SimGrid is to obtain a relative gain
+  greater than 1.
+  \AG{$t_\text{async} / t_\text{sync} > 1$, l'objectif est donc que Ã§a dure plus
+    longtemps (que Ã§a aille moins vite) en asynchrone qu'en synchrone ?
+    Ce n'est pas plutÃ´t l'inverse ?}
 \end{itemize}
 
-A priori, obtaining a relative gain greater than 1 would be difficult in a local area
-network configuration where the synchronous mode will take advantage on the
+A priori, obtaining a relative gain greater than 1 would be difficult in a local
+area network configuration where the synchronous mode will take advantage on the
 rapid exchange of information on such high-speed links. Thus, the methodology
 adopted was to launch the application on clustered network. In this last
-configuration, degrading the inter-cluster network performance will
-\textit{penalize} the synchronous mode allowing to get a relative gain greater than 1.
-This action simulates the case of distant clusters linked with long distance network
-like Internet.
-
+configuration, degrading the inter-cluster network performance will penalize the
+synchronous mode allowing to get a relative gain greater than 1.  This action
+simulates the case of distant clusters linked with long distance network like
+Internet.
+
+\AG{Cette partie sur le poisson 3D
+  % on sait donc que ce n'est pas une plie ou une sole (/me fatiguÃ©)
+  n'est pas Ã  sa place.  Elle devrait Ãªtre placÃ©e plus tÃ´t.}
 In this paper, we solve the 3D Poisson problem whose the mathematical model is 
 \begin{equation}
 \left\{
@@ -449,7 +468,7 @@ The parallel solving of the 3D Poisson problem with our multisplitting method re
 
 As a first step, the algorithm was run on a network consisting of two clusters
 containing 50 hosts each, totaling 100 hosts. Various combinations of the above
-factors have providing the results shown in Table~\ref{tab.cluster.2x50} with a
+factors have provided the results shown in Table~\ref{tab.cluster.2x50} with a
 matrix size ranging from $N_x = N_y = N_z = \text{62}$ to 171 elements or from
 $\text{62}^\text{3} = \text{\np{238328}}$ to $\text{171}^\text{3} =
 \text{\np{5000211}}$ entries.
@@ -470,10 +489,10 @@ $\text{62}^\text{3} = \text{\np{238328}}$ to $\text{171}^\text{3} =
 
   \begin{mytable}{6}
     \hline
-    bw
+    bandwidth
     & 5         & 5         & 5         & 5         & 5         & 50 \\
     \hline
-    lat
+    latency
     & 0.02      & 0.02      & 0.02      & 0.02      & 0.02      & 0.02 \\
     \hline
     power
@@ -483,21 +502,22 @@ $\text{62}^\text{3} = \text{\np{238328}}$ to $\text{171}^\text{3} =
     & 62        & 62        & 62        & 100       & 100       & 110 \\
     \hline
     Prec/Eprec
-    & \np{E-5}  & \np{E-8}  & \np{E-9}  & \np{E-11} & \np{E-11} & \np{E-11} \\
+    & \np{E-5}   & \np{E-8}  & \np{E-9}  & \np{E-11} & \np{E-11} & \np{E-11} \\
+    \hline
     \hline
     Relative gain
     & 2.52     & 2.55     & 2.52     & 2.57     & 2.54     & 2.53 \\
     \hline
   \end{mytable}
 
-  \smallskip
+  \bigskip
 
   \begin{mytable}{6}
     \hline
-    bw
+    bandwidth
     & 50        & 50        & 50        & 50        & 10        & 10 \\
     \hline
-    lat
+    latency
     & 0.02      & 0.02      & 0.02      & 0.02      & 0.03      & 0.01 \\
     \hline
     power
@@ -509,6 +529,7 @@ $\text{62}^\text{3} = \text{\np{238328}}$ to $\text{171}^\text{3} =
     Prec/Eprec
     & \np{E-11} & \np{E-11} & \np{E-11} & \np{E-11} & \np{E-5}  & \np{E-5} \\
     \hline
+    \hline
     Relative gain
     & 2.51     & 2.58     & 2.55     & 2.54     & 1.59      & 1.29 \\
     \hline
@@ -528,10 +549,10 @@ relative gains greater than 1 with a matrix size from 62 to 100 elements.
 
   \begin{mytable}{6}
     \hline
-    bw
+    bandwidth
     & 10       & 5        & 4        & 3        & 2        & 6 \\
     \hline
-    lat
+    latency
     & 0.01     & 0.02     & 0.02     & 0.02     & 0.02     & 0.02 \\
     \hline
     power
@@ -543,6 +564,7 @@ relative gains greater than 1 with a matrix size from 62 to 100 elements.
     Prec/Eprec
     & \np{E-5} & \np{E-5} & \np{E-5} & \np{E-5} & \np{E-5} & \np{E-5} \\
     \hline
+    \hline
     Relative gain
     & 1.003    & 1.01     & 1.08     & 1.19     & 1.28     & 1.01 \\
     \hline
@@ -560,9 +582,9 @@ Table~\ref{tab.cluster.3x67}.
 
   \begin{mytable}{1}
     \hline
-    bw         & 1 \\
+    bandwidth  & 1 \\
     \hline
-    lat        & 0.02 \\
+    latency    & 0.02 \\
     \hline
     power      & 1 \\
     \hline
@@ -570,6 +592,7 @@ Table~\ref{tab.cluster.3x67}.
     \hline
     Prec/Eprec & \np{E-5} \\
     \hline
+    \hline
     Relative gain    & 1.11 \\
     \hline
   \end{mytable}
@@ -581,10 +604,10 @@ Note that the program was run with the following parameters:
 
 ~\\{}\AG{Donner un peu plus de prÃ©cisions (plateforme en particulier).}
 \begin{itemize}
-	\item HOSTFILE: Hosts file description.
-	\item PLATFORM: file description of the platform architecture : clusters (CPU power,
-\dots{}), intra cluster network description, inter cluster network (bandwidth bw,
-lat latency, \dots{}).
+\item HOSTFILE: Hosts file description.
+\item PLATFORM: file description of the platform architecture : clusters (CPU
+  power, \dots{}), intra cluster network description, inter cluster network
+  (bandwidth, latency, \dots{}).
 \end{itemize}
 
 
@@ -618,7 +641,7 @@ increasing the matrix size up to 100 elements, it was necessary to increase the
 CPU power of \np[\%]{50} to \np[GFlops]{1.5} for a convergence of the algorithm
 with the same order of asynchronous mode efficiency.  Maintaining such a system
 power but this time, increasing network throughput inter cluster up to
-\np[Mbit/s]{50}, the result of efficiency with a relative gain of 1.5 is obtained with
+\np[Mbit/s]{50}, the result of efficiency with a relative gain of 1.5\AG[]{2.5 ?} is obtained with
 high external precision of \np{E-11} for a matrix size from 110 to 150 side
 elements.
 
@@ -629,6 +652,8 @@ matrix size of 62 elements, equality between the performance of the two modes
 (synchronous and asynchronous) is achieved with an inter cluster of
 \np[Mbit/s]{10} and a latency of \np[ms]{E-1}. To challenge an efficiency greater than 1.2 with a matrix size of 100 points, it was necessary to degrade the
 inter cluster network bandwidth from 5 to \np[Mbit/s]{2}.
+\AG{Conclusion, on prend une plateforme pourrie pour avoir un bon ratio sync/async ???
+  Quelle est la perte de perfs en faisant Ã§a ?}
 
 A last attempt was made for a configuration of three clusters but more powerful
 with 200 nodes in total. The convergence with a relative gain around 1.1 was
@@ -637,7 +662,7 @@ Table~\ref{tab.cluster.3x67}.
 
 \RC{Est ce qu'on sait expliquer pourquoi il y a une telle diffÃ©rence entre les rÃ©sultats avec 2 et 3 clusters... Avec 3 clusters, ils sont pas trÃ¨s bons... Je me demande s'il ne faut pas les enlever...}
 \RC{En fait je pense avoir la rÃ©ponse Ã  ma remarque... On voit avec les 2 clusters que le gain est d'autant plus grand qu'on choisit une bonne prÃ©cision. Donc, plusieurs solutions, lancer rapidement un long test pour confirmer ca, ou enlever des tests... ou on ne change rien :-)}
-\LZK{Ma question est: le bw et lat sont ceux inter-clusters ou pour les deux inter et intra cluster??}
+\LZK{Ma question est: le bandwidth et latency sont ceux inter-clusters ou pour les deux inter et intra cluster??}
 
 \section{Conclusion}
 The experimental results on executing a parallel iterative algorithm in 
@@ -698,3 +723,5 @@ This work is partially funded by the Labex ACTION program (contract ANR-11-LABX-
 % LocalWords:  Parallelization AIAC GMRES multi SMPI SISC SIAC SimDAG DAGs Lua
 % LocalWords:  Fortran GFlops priori Mbit de du fcomte multisplitting scalable
 % LocalWords:  SimGrid Belfort parallelize Labex ANR LABX IEEEabrv hpccBib
+% LocalWords:  intra durations nonsingular Waitall discretization discretized
+% LocalWords:  InnerSolver Isend Irecv
