an approximate value $X^*$ of the solution with a very low
residual error. Several well-known methods demonstrate the convergence
of these algorithms. Generally, to reduce the complexity and the
-execution time, the problem is divided into several "pieces" that will
+execution time, the problem is divided into several \emph{pieces} that will
be solved in parallel on multiple processing units. The latter will
communicate each intermediate results before a new iteration starts
until the approximate solution is reached. These distributed parallel
-computations can be performed either in "synchronous" communication mode
+computations can be performed either in \emph{synchronous} communication mode
where a new iteration begin only when all nodes communications are
-completed, either "asynchronous" mode where processors can continue
+completed, either \emph{asynchronous} mode where processors can continue
independently without or few synchronization points. Despite the
effectiveness of iterative approach, a major drawback of the method is
the requirement of huge resources in terms of computing capacity,
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 "penalize" the synchronous
+degrading the inter-cluster network performance will \emph{penalize} the synchronous
mode allowing to get a speedup lower than 1. This action simulates the case of
clusters linked with long distance network like Internet.
