On distributed memory MPI-based parallelization of SPH codes in massive HPC context

Most of particle methods share the problem of high computational cost and in order to satisfy the demands of solvers, currently available hardware technologies must be fully exploited. Two complementary technologies are now accessible. On the one hand, CPUs which can be structured into a multi-node framework, allowing massive data exchanges through a high speed network. In this case, each node is usually comprised of several cores available to perform multithreaded computations. On the other hand, GPUs which are derived from the graphics computing technologies, able to perform highly multithreaded calculations with hundreds of independent threads connected together through a common shared memory. This paper is primarily dedicated to the distributed memory parallelization of particle methods, targeting several thousands of CPU cores. The experience gained clearly shows that parallelizing a particle-based code on moderate numbers of cores can easily lead to an acceptable scalability, whilst a scalable speedup on thousands of cores is much more difficult to obtain. The discussion revolves around speeding up particle methods as a whole, in a massive HPC context by making use of the MPI library. We focus on one particular particle method which is Smoothed Particle Hydrodynamics (SPH), one of the most widespread today in the literature as well as in engineering. (C) 2015 Published by Elsevier B.V.