Abstract
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 multi-threaded 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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.