  The aim of this paper is to present a first evaluation of the potential of an
asynchronous distributed computation associated to the recently proposed
approach, D-iteration: the D-iteration is a fluid diffusion based iterative
method, which has the advantage of being natively distributive. It exploits a
simple intuitive decomposition of the matrix-vector product as elementary
operations of fluid diffusion associated to a new algebraic representation. We
show through experiments on real datasets how much this approach can improve
the computation efficiency when the parallelism is applied: with the proposed
solution, when the computation is distributed over $K$ virtual machines (PIDs),
the memory size to be handled by each virtual machine decreases linearly with
$K$ and the computation speed increases almost linearly with $K$ with a slope
becoming closer to one when the number $N$ of linear equations to be solved
increases.
