X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/hpcc2014.git/blobdiff_plain/5f0dd22ada037b4a0657d93645f08e99403867e2..5303b288b71ba1b55f6f784f6ab4aba3db64cf22:/hpcc.tex

diff --git a/hpcc.tex b/hpcc.tex
index b9b1753..1c94f84 100644
--- a/hpcc.tex
+++ b/hpcc.tex
@@ -358,12 +358,15 @@ clusters linked with long distance network like Internet.
 
 As a first step, the algorithm was run on a network consisting of two clusters
 containing fifty hosts each, totaling one hundred hosts. Various combinations of
-the above factors have providing the results shown in Table I with a matrix size
-ranging from $N_x = N_y = N_z = 62 \text{ to } 171$ elements or from $62^{3} = \np{238328}$ to
-$171^{3} = \np{5211000}$ entries.
+the above factors have providing the results shown in
+Table~\ref{tab.cluster.2x50} with a matrix size ranging from $N_x = N_y = N_z =
+62 \text{ to } 171$ elements or from $62^{3} = \np{238328}$ to $171^{3} =
+\np{5211000}$ entries.
 
-\begin{table}[h!]
- \centering
+\begin{table}
+  \centering
+  \caption{2 Clusters x 50 nodes each}
+  \label{tab.cluster.2x50}
 
  \tiny
  
@@ -377,18 +380,18 @@ $171^{3} = \np{5211000}$ entries.
  \bf Prec/Eprec & 10$^{-5}$ & 10$^{-8}$ & 10$^{-9}$ & 10$^{-11}$ & 10$^{-11}$ & 10$^{-11}$ & 10$^{-11}$ & 10$^{-11}$ & 10$^{-11}$ & 10$^{-11}$ & 10$^{-5}$ & 10$^{-5}$\\ \hline 
  \bf speedup & 0.396 & 0.392 & 0.396 & 0.391 & 0.393 & 0.395 & 0.398 & 0.388 & 0.393 & 0.394 & 0.63 & 0.778\\ \hline 
  \end{tabular}
- \smallskip 
- \caption{2 Clusters x 50 nodes each} \label{tab1} 
 \end{table} 
   
 Then we have changed the network configuration using three clusters containing
 respectively 33, 33 and 34 hosts, or again by on hundred hosts for all the
 clusters. In the same way as above, a judicious choice of key parameters has
-permitted to get the results in Table II which shows the speedups less than 1 with
-a matrix size from 62 to 100 elements.
+permitted to get the results in Table~\ref{tab.cluster.3x33} which shows the
+speedups less than 1 with a matrix size from 62 to 100 elements.
 
-\begin{table}[h!]
- \centering
+\begin{table}
+  \centering
+  \caption{3 Clusters x 33 nodes each}
+  \label{tab.cluster.3x33}
 
  \tiny
  
@@ -403,16 +406,18 @@ a matrix size from 62 to 100 elements.
  \bf speedup & 0.997 & 0.99 & 0.93 & 0.84 & 0.78 & 0.99\\ 
  \hline 
  \end{tabular}
- \smallskip 
- \caption{3 Clusters x 33 nodes each} \label{tab2} 
 \end{table} 
 
 
 In a final step, results of an execution attempt to scale up the three clustered
-configuration but increasing by two hundreds hosts has been recorded in Table III.
+configuration but increasing by two hundreds hosts has been recorded in
+Table~\ref{tab.cluster.3x67}.
+
+\begin{table}
+  \centering
+  \caption{3 Clusters x 66 nodes each}
+  \label{tab.cluster.3x67}
 
-\begin{table}[h!]
- \centering
  \tiny
 \begin{tabular}{|M{0.55cm}|M{0.25cm}|} 
  \hline 
@@ -428,8 +433,6 @@ configuration but increasing by two hundreds hosts has been recorded in Table II
  \bf speedup & 0.9\\ 
  \hline 
  \end{tabular}
- \smallskip 
- \caption{3 Clusters x 66 nodes each} \label{tab3} 
 \end{table} 
 
 Note that the program was run with the following parameters:
@@ -455,39 +458,41 @@ lat latency, \dots{}).
 	\item Execution Mode: synchronous or asynchronous.
 \end{itemize}
 
-
 \paragraph*{Interpretations and comments}
 
 After analyzing the outputs, generally, for the configuration with two or three
-clusters including one hundred hosts (Table I and II), some combinations of the
-used parameters affecting the results have given a speedup less than 1, showing
-the effectiveness of the asynchronous performance compared to the synchronous
-mode.
-
-In the case of a two clusters configuration, Table I shows that with a
-deterioration of inter cluster network set with \np[Mbits/s]{5} of bandwidth, a latency
-in order of a hundredth of a millisecond and a system power of one GFlops, an
-efficiency of about \np[\%]{40} in asynchronous mode is obtained for a matrix size of 62
-elements. It is noticed that the result remains stable even if we vary the
-external precision from \np{E-5} to \np{E-9}. By increasing the problem 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[Mbits/s]{50}, the result of efficiency of about \np[\%]{40} is
-obtained with high external  precision of \np{E-11} for a matrix size from 110 to 150
-side elements.
-
-For the 3 clusters architecture including a total of 100 hosts, Table II shows
-that it was difficult to have a combination which gives an efficiency of
-asynchronous below \np[\%]{80}. Indeed, for a matrix size of 62 elements, equality
-between the performance of the two modes (synchronous and asynchronous) is
-achieved with an inter cluster of \np[Mbits/s]{10} and a latency of \np[ms]{E-1}. To
-challenge an efficiency by \np[\%]{78} with a matrix size of 100 points, it was
-necessary to degrade the inter cluster network bandwidth from 5 to 2 Mbit/s.
+clusters including one hundred hosts (Tables~\ref{tab.cluster.2x50}
+and~\ref{tab.cluster.3x33}), some combinations of the used parameters affecting
+the results have given a speedup less than 1, showing the effectiveness of the
+asynchronous performance compared to the synchronous mode.
+
+In the case of a two clusters configuration, Table~\ref{tab.cluster.2x50} shows
+that with a deterioration of inter cluster network set with \np[Mbits/s]{5} of
+bandwidth, a latency in order of a hundredth of a millisecond and a system power
+of one GFlops, an efficiency of about \np[\%]{40} in asynchronous mode is
+obtained for a matrix size of 62 elements. It is noticed that the result remains
+stable even if we vary the external precision from \np{E-5} to \np{E-9}. By
+increasing the problem 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[Mbits/s]{50}, the result of efficiency of about \np[\%]{40} is obtained with
+high external precision of \np{E-11} for a matrix size from 110 to 150 side
+elements.
+
+For the 3 clusters architecture including a total of 100 hosts,
+Table~\ref{tab.cluster.3x33} shows that it was difficult to have a combination
+which gives an efficiency of asynchronous below \np[\%]{80}. Indeed, for a
+matrix size of 62 elements, equality between the performance of the two modes
+(synchronous and asynchronous) is achieved with an inter cluster of
+\np[Mbits/s]{10} and a latency of \np[ms]{E-1}. To challenge an efficiency by
+\np[\%]{78} with a matrix size of 100 points, it was necessary to degrade the
+inter cluster network bandwidth from 5 to 2 Mbit/s.
 
 A last attempt was made for a configuration of three clusters but more powerful
-with 200 nodes in total. The convergence with a speedup of \np[\%]{90} was obtained
-with a bandwidth of \np[Mbits/s]{1} as shown in Table III.
+with 200 nodes in total. The convergence with a speedup of \np[\%]{90} was
+obtained with a bandwidth of \np[Mbits/s]{1} as shown in
+Table~\ref{tab.cluster.3x67}.
 
 \section{Conclusion}
 The experimental results on executing a parallel iterative algorithm in 
