fin correct

[rce2015.git] / paper.tex

diff --git a/paper.tex b/paper.tex
index 67599b9a912632d8b41122c3b57fa6a06fb8da34..bc1b1b4c9870e3f8a12a9a0f203ca84ef1160653 100644 (file)
--- a/paper.tex
+++ b/paper.tex
@@ -488,7 +488,7 @@ SimGrid  to simulate the  grid environment.
 \paragraph{Simulation parameters for SimGrid}
 \  \\ \noindent  Before running  a SimGrid  benchmark, many  parameters for  the
 computation platform must be defined. For our experiments, we consider platforms
 \paragraph{Simulation parameters for SimGrid}
 \  \\ \noindent  Before running  a SimGrid  benchmark, many  parameters for  the
 computation platform must be defined. For our experiments, we consider platforms

-in which  several clusters are  geographically distant,  so there are  intra and
+in which  several clusters are  geographically distant,  so that there are  intra and

 inter-cluster communications. In the following, these parameters are described:
 
 \begin{itemize}
 inter-cluster communications. In the following, these parameters are described:
 
 \begin{itemize}


@@ -563,8 +563,8 @@ the application to be tested. Numerical parallel iterative algorithms
 have been chosen for the study in this paper. \\
 
 \textbf{Step 2}: Collect the software materials needed for the experimentation.
 have been chosen for the study in this paper. \\
 
 \textbf{Step 2}: Collect the software materials needed for the experimentation.

-In our case, we have two variants algorithms for the resolution of the
-3D-Poisson problem: (1) using the classical GMRES; (2) and the multisplitting
+In our case, we have two variants for the resolution of the
+3D-Poisson problem: (1) using the classical GMRES; (2) using the multisplitting

 method. In addition, the SimGrid simulator has been chosen to simulate the
 behaviors of the distributed applications. SimGrid is running in a virtual
 machine on a simple laptop. \\
 method. In addition, the SimGrid simulator has been chosen to simulate the
 behaviors of the distributed applications. SimGrid is running in a virtual
 machine on a simple laptop. \\


@@ -580,7 +580,7 @@ have been configured in SimGrid : 2$\times$16, 4$\times$8, 4$\times$16, 8$\times
 represents the number  of clusters in the grid and  the second number represents
 the number  of hosts (processors/cores)  in each  cluster. \\
 
 represents the number  of clusters in the grid and  the second number represents
 the number  of hosts (processors/cores)  in each  cluster. \\
 

-\textbf{Step 5}: Conduct an extensive and comprehensive testings
+\textbf{Step 5}: Conduct  extensive and comprehensive testings

 within these configurations by varying the key parameters, especially
 the CPU power capacity, the network parameters and also the size of the
 input data.  \\
 within these configurations by varying the key parameters, especially
 the CPU power capacity, the network parameters and also the size of the
 input data.  \\


@@ -600,39 +600,38 @@ Another important factor  impacting the overall performance  of the application
 is the network configuration. Two main network parameters can modify drastically
 the program output results:
 \begin{enumerate}
 is the network configuration. Two main network parameters can modify drastically
 the program output results:
 \begin{enumerate}

-\item  the network  bandwidth  ($bw$ in bits/s) also  known  as "the  data-carrying
-    capacity" of the network is defined as  the maximum of data that can transit
+\item  the network  bandwidth  ($bw$ in Gbits/s) also  known  as "the  data-carrying
+    capacity" of the network is defined as  the maximum amount of data that can transit

     from one point to another in a unit of time.
 \item the  network latency  ($lat$ in microseconds) defined as  the delay  from the
     from one point to another in a unit of time.
 \item the  network latency  ($lat$ in microseconds) defined as  the delay  from the

-  start time to send  a simple data from a source to a destination.
+  starting time to send  a simple data from a source to a destination.

 \end{enumerate}
 \end{enumerate}

-Upon  the   network  characteristics,  another  impacting   factor  is  the volume of data exchanged  between the nodes in the cluster
+Among  the   network  characteristics,  another  impacting   factor  is  the volume of data exchanged  between the nodes in the cluster

 and  between distant  clusters.  This parameter is application dependent.
 
 and  between distant  clusters.  This parameter is application dependent.
 

- In  a grid  environment, it  is common  to distinguish,  on one hand,  the
+ In  a grid  environment, it  is common  to distinguish,  on the one hand,  the

  \textit{intra-network} which refers  to the links between nodes within  a
  cluster and on  the other  hand, the  \textit{inter-network} which  is the
  backbone link  between clusters.  In   practice,  these  two   networks  have
  different   speeds. The intra-network  generally works  like a  high speed
  \textit{intra-network} which refers  to the links between nodes within  a
  cluster and on  the other  hand, the  \textit{inter-network} which  is the
  backbone link  between clusters.  In   practice,  these  two   networks  have
  different   speeds. The intra-network  generally works  like a  high speed

- local network  with a high bandwidth and very low latency. In opposite, the
- inter-network connects clusters sometime via  heterogeneous networks components
- through internet with a lower speed.  The network  between distant  clusters
+ local network  with a high bandwidth and very low latency. On the contrary, the
+ inter-network connects clusters sometimes via  heterogeneous networks components the through internet with a lower speed.  The network  between distant  clusters

  might  be a  bottleneck for  the global performance of the application.
 
 
 \subsection{Comparison between GMRES and two-stage multisplitting algorithms in
 synchronous mode}
 In the scope of this paper, our first objective is to analyze
  might  be a  bottleneck for  the global performance of the application.
 
 
 \subsection{Comparison between GMRES and two-stage multisplitting algorithms in
 synchronous mode}
 In the scope of this paper, our first objective is to analyze

-when the synchronous Krylov two-stage method has better performance than the
-classical GMRES method. With a synchronous iterative method, better performance
-means a smaller number of iterations and execution time before reaching the
+when the synchronous Krylov two-stage method has better performances than the
+classical GMRES method. With a synchronous iterative method, better performances
+mean a smaller number of iterations and execution time before reaching the

 convergence.
 
 Table~\ref{tab:01} summarizes the parameters used in the different simulations:
 the grid architectures (i.e. the number of clusters and the number of nodes per
 cluster), the network of inter-clusters backbone links and the matrix sizes of
 the 3D Poisson problem. However, for all simulations we fix the network
 convergence.
 
 Table~\ref{tab:01} summarizes the parameters used in the different simulations:
 the grid architectures (i.e. the number of clusters and the number of nodes per
 cluster), the network of inter-clusters backbone links and the matrix sizes of
 the 3D Poisson problem. However, for all simulations we fix the network

-parameters of the intra-clusters links: the bandwidth $bw$=10Gbs and the latency
+parameters of the intra-clusters links: the bandwidth $bw$=10Gbit/s and the latency

 $lat=8\mu$s. In what follows, we will present the test conditions, the output
 results and our comments.
 
 $lat=8\mu$s. In what follows, we will present the test conditions, the output
 results and our comments.
 


@@ -641,8 +640,8 @@ results and our comments.
 \begin{tabular}{ll}
 \hline
 Grid architecture                       & 2$\times$16, 4$\times$8, 4$\times$16 and 8$\times$8\\
 \begin{tabular}{ll}
 \hline
 Grid architecture                       & 2$\times$16, 4$\times$8, 4$\times$16 and 8$\times$8\\

-\multirow{2}{*}{Network inter-clusters} & $N1$: $bw$=10Gbs, $lat=8\mu$s \\
-                                        & $N2$: $bw$=1Gbs, $lat=50\mu$s \\
+\multirow{2}{*}{Network inter-clusters} & $N1$: $bw$=10Gbit/s, $lat=8\mu$s \\
+                                        & $N2$: $bw$=1Gbit/s, $lat=50\mu$s \\

 \multirow{2}{*}{Matrix size}            & $Mat1$: N$_{x}\times$N$_{y}\times$N$_{z}$=150$\times$150$\times$150\\
                                         & $Mat2$: N$_{x}\times$N$_{y}\times$N$_{z}$=170$\times$170$\times$170 \\ \hline
 \end{tabular}
 \multirow{2}{*}{Matrix size}            & $Mat1$: N$_{x}\times$N$_{y}\times$N$_{z}$=150$\times$150$\times$150\\
                                         & $Mat2$: N$_{x}\times$N$_{y}\times$N$_{z}$=170$\times$170$\times$170 \\ \hline
 \end{tabular}


@@ -660,16 +659,16 @@ Table~\ref{tab:01}). Figure~\ref{fig:01} shows, for all grid configurations and
 a given matrix size of 170$^3$ elements, a  non-variation in the number of
 iterations for the classical GMRES algorithm, which is not the case of the
 Krylov two-stage algorithm. In fact, with multisplitting  algorithms, the number
 a given matrix size of 170$^3$ elements, a  non-variation in the number of
 iterations for the classical GMRES algorithm, which is not the case of the
 Krylov two-stage algorithm. In fact, with multisplitting  algorithms, the number

-of splitting (in our case, it is equal to the number of clusters) influences on the
-convergence speed. The higher the number  of splitting is, the slower the
+of splittings (in our case, it is equal to the number of clusters) influences on the
+convergence speed. The higher the number  of splittings is, the slower the

 convergence of the algorithm is (see the output results obtained from
 configurations 2$\times$16 vs. 4$\times$8 and configurations 4$\times$16 vs.
 8$\times$8).
 
 convergence of the algorithm is (see the output results obtained from
 configurations 2$\times$16 vs. 4$\times$8 and configurations 4$\times$16 vs.
 8$\times$8).
 

-The execution times between both algorithms is significant with different grid
+The execution times between both algorithms are significant with different grid

 architectures. The synchronous Krylov two-stage algorithm presents better
 architectures. The synchronous Krylov two-stage algorithm presents better

-performances than the GMRES algorithm, even for a high number of clusters (about
-$32\%$ more efficient on a grid of 8$\times$8 than GMRES). In addition, we can
+performances than the GMRES algorithm, even for a high number of clusters (it is about
+$32\%$ more efficient on a grid of 8$\times$8 than the GMRES). In addition, we can

 observe a better sensitivity of the Krylov two-stage algorithm (compared to the
 GMRES one) when scaling up the number of the processors in the computational
 grid: the Krylov two-stage algorithm is about $48\%$ and the GMRES algorithm is
 observe a better sensitivity of the Krylov two-stage algorithm (compared to the
 GMRES one) when scaling up the number of the processors in the computational
 grid: the Krylov two-stage algorithm is about $48\%$ and the GMRES algorithm is


@@ -705,7 +704,7 @@ efficient for distributed systems with high latency networks.
 \end{figure}
 
 \subsubsection{Network latency impacts on performances\\}
 \end{figure}
 
 \subsubsection{Network latency impacts on performances\\}

-Figure~\ref{fig:03} shows the impact of the network latency on the performances of both algorithms. The simulation is conducted on a computational grid of 2 clusters of 16 processors each (i.e. configuration 2$\times$16) interconnected by a network of bandwidth $bw$=1Gbs to solve a 3D Poisson problem of size $150^3$. According to the results, a degradation of the network latency from $8\mu$s to $60\mu$s implies an absolute execution time increase for both algorithms, but not with the same rate of degradation. The GMRES algorithm is more sensitive to the latency degradation than the Krylov two-stage algorithm.
+Figure~\ref{fig:03} shows the impact of the network latency on the performances of both algorithms. The simulation is conducted on a computational grid of 2 clusters of 16 processors each (i.e. configuration 2$\times$16) interconnected by a network of bandwidth $bw$=1Gbit/s to solve a 3D Poisson problem of size $150^3$. According to the results, a degradation of the network latency from $8\mu$s to $60\mu$s implies an absolute execution time increase for both algorithms, but not with the same rate of degradation. The GMRES algorithm is more sensitive to the latency degradation than the Krylov two-stage algorithm.

 
 \begin{figure}[ht]
 \centering
 
 \begin{figure}[ht]
 \centering


@@ -718,9 +717,8 @@ Figure~\ref{fig:03} shows the impact of the network latency on the performances
 
 Figure~\ref{fig:04} reports the results obtained for the simulation of a grid of
 $2\times16$ processors interconnected by a network of latency $lat=50\mu$s to
 
 Figure~\ref{fig:04} reports the results obtained for the simulation of a grid of
 $2\times16$ processors interconnected by a network of latency $lat=50\mu$s to

-solve a 3D Poisson problem of size $150^3$. The results of increasing the
-network bandwidth from $1$Gbs to $10$Gbs show the performances improvement for
-both algorithms by reducing the execution times. However, the Krylov two-stage
+solve a 3D Poisson problem of size $150^3$. Increasing the
+network bandwidth from $1$Gbit/s to $10$Gbit/s results in improving the performances of both algorithms by reducing the execution times. However, the Krylov two-stage

 algorithm presents a better performance gain in the considered bandwidth
 interval with a gain of $40\%$ compared to only about $24\%$ for the classical
 GMRES algorithm.
 algorithm presents a better performance gain in the considered bandwidth
 interval with a gain of $40\%$ compared to only about $24\%$ for the classical
 GMRES algorithm.


@@ -734,7 +732,7 @@ GMRES algorithm.
 
 \subsubsection{Matrix size impacts on performances\\}
 
 
 \subsubsection{Matrix size impacts on performances\\}
 

-In these experiments, the matrix size of the 3D Poisson problem is varied from
+In these experiments, the matrix size of the 3D Poisson problem varies from

 $50^3$ to $190^3$ elements. The simulated computational grid is composed of $4$
 clusters of $8$ processors each interconnected by the network $N2$ (see
 Table~\ref{tab:01}). As shown in Figure~\ref{fig:05}, the execution
 $50^3$ to $190^3$ elements. The simulated computational grid is composed of $4$
 clusters of $8$ processors each interconnected by the network $N2$ (see
 Table~\ref{tab:01}). As shown in Figure~\ref{fig:05}, the execution


@@ -745,7 +743,7 @@ bigger the ratio between execution times of both algorithms is. We can also
 observe that for some problem sizes, the convergence (and thus the execution
 time) of the Krylov two-stage algorithm varies quite a lot.
 %This is due to the 3D partitioning of the 3D matrix of the Poisson problem.
 observe that for some problem sizes, the convergence (and thus the execution
 time) of the Krylov two-stage algorithm varies quite a lot.
 %This is due to the 3D partitioning of the 3D matrix of the Poisson problem.

-These findings may help a lot end users to setup the best and the optimal targeted environment for the application deployment when focusing on the problem size scale up.
+These findings may greatly help a lot end users to setup the best and the optimal targeted environment for the application deployment when focusing on the problem size scale up.

 
 \begin{figure}[ht]
 \centering
 
 \begin{figure}[ht]
 \centering


@@ -775,14 +773,14 @@ processors.
 
 To conclude these series of experiments, with  SimGrid we have been able to make
 many simulations  with many parameters  variations. Doing all  these experiments
 
 To conclude these series of experiments, with  SimGrid we have been able to make
 many simulations  with many parameters  variations. Doing all  these experiments

-with a real platform is most of the time not possible or very costly. Moreover
+with a real platform is most of the time impossible or very costly. Moreover

 the behavior of both GMRES and  Krylov two-stage algorithms is in accordance
 with larger real executions on large scale supercomputers~\cite{couturier15}.
 
 
 \subsection{Comparison between synchronous GMRES and asynchronous two-stage multisplitting algorithms}
 
 the behavior of both GMRES and  Krylov two-stage algorithms is in accordance
 with larger real executions on large scale supercomputers~\cite{couturier15}.
 
 
 \subsection{Comparison between synchronous GMRES and asynchronous two-stage multisplitting algorithms}
 

-The previous paragraphs  put in evidence the interests to  simulate the behavior
+The previous paragraphs  put in evidence the interest to  simulate the behavior

 of  the application  before  any  deployment in  a  real  environment.  In  this
 section, following  the same previous  methodology, our  goal is to  compare the
 efficiency of the multisplitting method  in \textit{ asynchronous mode} compared with the
 of  the application  before  any  deployment in  a  real  environment.  In  this
 section, following  the same previous  methodology, our  goal is to  compare the
 efficiency of the multisplitting method  in \textit{ asynchronous mode} compared with the


@@ -791,8 +789,8 @@ classical GMRES in \textit{synchronous mode}.
 The  interest of  using  an asynchronous  algorithm  is that  there  is no  more
 synchronization. With  geographically distant  clusters, this may  be essential.
 In  this case,  each  processor can  compute its  iterations  freely without  any
 The  interest of  using  an asynchronous  algorithm  is that  there  is no  more
 synchronization. With  geographically distant  clusters, this may  be essential.
 In  this case,  each  processor can  compute its  iterations  freely without  any

-synchronization  with   the  other   processors.  Thus,  the   asynchronous  may
-theoretically reduce  the overall execution  time and can improve  the algorithm
+synchronization  with   the  other   processors.  Thus,  an   asynchronous algorithm  may
+theoretically reduce  the overall execution  time and can also improve  the algorithm

 performance.
 
 In this section,  the SimGrid simulator is  used to compare the  behavior of the
 performance.
 
 In this section,  the SimGrid simulator is  used to compare the  behavior of the


@@ -867,8 +865,8 @@ summarized in Table~\ref{tab:02}.
 Table~\ref{tab:03} reports  the relative gains  between both algorithms.   It is
 defined by the ratio between the execution  time of GMRES and the execution time
 of the  multisplitting. The ratio is  greater than one because  the asynchronous
 Table~\ref{tab:03} reports  the relative gains  between both algorithms.   It is
 defined by the ratio between the execution  time of GMRES and the execution time
 of the  multisplitting. The ratio is  greater than one because  the asynchronous

-multisplitting  version  is  faster  than   GMRES.  In  average,  the  two-stage
-multisplitting algorithm to  be more than $2.5$ times faster  than the classical
+multisplitting  version  is  faster  than   GMRES.  On  average,  the  two-stage
+multisplitting algorithm is 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 geographically
 distant clusters through the internet.
 GMRES.  These experiments also show the relative tolerance of the multisplitting
 algorithm when using a low speed network as usually observed with geographically
 distant clusters through the internet.


@@ -887,19 +885,19 @@ 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
 
 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
+multi-core  architecture. Finding   good  resource allocation 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
 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.
+convergeence and consequently to very different execution times.

 
 
 In future works, we  plan to investigate how to simulate  the behavior of really
 
 
 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
+large scale  applications. For  example, if  we are  interested in  simulating the

 execution of the solvers of this paper with thousand or even dozens of thousands
 of cores,  it is not possible  to do that with  SimGrid. In fact, this  tool will
 execution of the solvers of this paper with thousand or even dozens of thousands
 of cores,  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.
+make the real computation. That is why, we plan to focus our research on that problematic.