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 40 \% for the algorithm
+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 E-11. Such successful results open
+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.
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{E-1} ms. To
+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.
\setcounter{numberedCntD}{\theenumi}
\end{enumerate}
Our results have shown that in certain conditions, asynchronous mode is
-speeder up to 40 \% than executing the algorithm in synchronous mode
+speeder up to \np[\%]{40} than executing the algorithm in synchronous mode
which is not negligible for solving complex practical problems with more
and more increasing size.
