network. Parameters on the cluster's architecture are the number of machines and
the computation power of a machine. Simulations show that the asynchronous
multisplitting algorithm can solve the 3D Poisson problem approximately twice
-faster than GMRES with two distant clusters.
+faster than GMRES with two distant clusters. In this way, we present an original solution to optimize the use of a simulation
+tool to run efficiently an asynchronous iterative parallel algorithm in a grid architecture
\begin{table}[!t]
\centering
\caption{Relative gain of the multisplitting algorithm compared to GMRES for
- different configurations with 2 clusters, each one composed of 50 nodes.}
+ different configurations with 2 clusters, each one composed of 50 nodes. Latency = $20$ms}
\label{tab.cluster.2x50}
\begin{mytable}{5}
bandwidth (Mbit/s)
& 5 & 5 & 5 & 5 & 5 \\
\hline
- latency (ms)
- & 20 & 20 & 20 & 20 & 20 \\
- \hline
+ % latency (ms)
+ % & 20 & 20 & 20 & 20 & 20 \\
+ %\hline
power (GFlops)
& 1 & 1 & 1 & 1.5 & 1.5 \\
\hline
size $(N)$
- & 62 & 62 & 62 & 100 & 100 \\
+ & $62^3$ & $62^3$ & $62^3$ & $100^3$ & $100^3$ \\
\hline
Precision
& \np{E-5} & \np{E-8} & \np{E-9} & \np{E-11} & \np{E-11} \\
bandwidth (Mbit/s)
& 50 & 50 & 50 & 50 & 50 \\ % & 10 & 10 \\
\hline
- latency (ms)
- & 20 & 20 & 20 & 20 & 20 \\ % & 0.03 & 0.01 \\
- \hline
+ %latency (ms)
+ %& 20 & 20 & 20 & 20 & 20 \\ % & 0.03 & 0.01 \\
+ %\hline
Power (GFlops)
& 1.5 & 1.5 & 1.5 & 1.5 & 1.5 \\ % & 1 & 1.5 \\
\hline
size $(N)$
- & 110 & 120 & 130 & 140 & 150 \\ % & 171 & 171 \\
+ & $110^3$ & $120^3$ & $130^3$ & $140^3$ & $150^3$ \\ % & 171 & 171 \\
\hline
Precision
& \np{E-11} & \np{E-11} & \np{E-11} & \np{E-11} & \np{E-11} \\ % & \np{E-5} & \np{E-5} \\
\end{mytable}
\end{table}
-\RC{Du coup la latence est toujours la même, pourquoi la mettre dans la table?}
+%\RC{Du coup la latence est toujours la même, pourquoi la mettre dans la 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
asynchronous multisplitting compared to GMRES with two distant clusters.
With these settings, Table~\ref{tab.cluster.2x50} shows
-that after setting the bandwidth of the inter cluster network to \np[Mbit/s]{5} and a latency in order of one hundredth of millisecond and a processor power
-of one GFlops, an efficiency of about \np[\%]{40} is
+that after setting the bandwidth of the inter cluster network to \np[Mbit/s]{5}, the latency to $20$ millisecond and the processor power
+to one GFlops, an efficiency of about \np[\%]{40} is
obtained in asynchronous mode for a matrix size of $62^3$ elements. It is noticed that the result remains
stable even we vary the residual error precision from \np{E-5} to \np{E-9}. By
increasing the matrix size up to $100^3$ elements, it was necessary to increase the
%(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 ça ?}
+%\AG{Conclusion, on prend une plateforme pourrie pour avoir un bon ratio sync/async ???
+ %Quelle est la perte de perfs en faisant ça ?}
%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
%\CER{Définitivement, les paramètres réseaux variables ici se rapportent au réseau INTER cluster.}
\section{Conclusion}
The simulation of the execution of parallel asynchronous iterative algorithms on large scale clusters has been presented.
-In this work, we show that SIMGRID is an efficient simulation tool that allows us to
+In this work, we show that SimGrid is an efficient simulation tool that allows us to
reach the following two objectives:
\begin{enumerate}
mode in a grid architecture.
In future works, we plan to extend our experimentations to larger scale platforms by increasing the number of computing cores and the number of clusters.
-We will also have to increase the size of the input problem which will require the use of a more powerful simulation platform. At last, we expect to compare our simulation results to real execution results on real architectures in order to experimentally validate our study. Finally, we also plan to study other problems with the multisplitting method and other asynchronous iterative methods.
+We will also have to increase the size of the input problem which will require the use of a more powerful simulation platform. At last, we expect to compare our simulation results to real execution results on real architectures in order to better experimentally validate our study. Finally, we also plan to study other problems with the multisplitting method and other asynchronous iterative methods.
\section*{Acknowledgment}
