-\item ``Follow up on point 1). The experiment section can be enhanced as well. The numbers presented are very specific to the input matrix workload, which is generated by the authors. So it is unclear how much other researchers can benefit from it. It will be nice to focus on more detailed measuring and metrics, i.e., how to evaluate if your algorithm/optimization has maximally exploited the system capacity based on the CPU/GPU power and bandwidth available? Or is your algorithm as presented is the optimal at all?'' \\ \\ The sparse matrices that we have found in the literature are very small for our experiments and they don't allow to exploit the computing power of a GPU cluster. This is why we used a generator of large sparse matrices based on the real-world matrices of the Davis collection of the Florida university.
+\item ``Follow up on point 1). The experiment section can be enhanced as well. The numbers presented are very specific to the input matrix workload, which is generated by the authors. So it is unclear how much other researchers can benefit from it. It will be nice to focus on more detailed measuring and metrics, i.e., how to evaluate if your algorithm/optimization has maximally exploited the system capacity based on the CPU/GPU power and bandwidth available? Or is your algorithm as presented is the optimal at all?'' \\ \\ The sparse matrices that we have found in the literature are very small for our experiments and they don't allow to exploit the computing power of a GPU cluster. This is why we used a generator of large sparse matrices based on the real-world matrices of the Davis collection of the Florida university. Of course, you need to make a choice of the experiments to performed. However, with we have chosen different matrices with differents patterns that induce either few or many communications. As explained previously, it is not possible to make an algorithm for solving linear system which is optimal in any situation. In this work we concentred our effort on the parallelization on a GPU cluster. Nevertheless there are hundred of variants of the GMRES method. It would be surprising that quite old methods that seemed not very interesting may not have a new interest with GPU clusters. Moreover, according to the nature of the matrix some specific solvers have been build to take advantage of these specificities. For other kinds of architectures, researchers did not tried to optimize all the parameters since this is too difficult and since the number of parameters is really important. So, a method that would try to optimize them would take more time than simply solving the linear system.
