A Flexible Patch-Based Lattice Boltzmann Parallelization Approach for Heterogeneous GPU-CPU Clusters

Sustaining a large fraction of single GPU performance in parallel computations is considered to be the major problem of GPU-based clusters. In this article, this topic is addressed in the context of a lattice Boltzmann flow solver that is integrated in the WaLBerla software framework. We propose a multi-GPU implementation using a block-structured MPI parallelization, suitable for load balancing and heterogeneous computations on CPUs and GPUs. The overhead required for multi-GPU simulations is discussed in detail and it is demonstrated that the kernel performance can be sustained to a large extent. With our GPU implementation, we achieve nearly perfect weak scalability on InfiniBand clusters. However, in strong scaling scenarios multi-GPUs make less efficient use of the hardware than IBM BG/P and x86 clusters. Hence, a cost analysis must determine the best course of action for a particular simulation task. Additionally, weak scaling results of heterogeneous simulations conducted on CPUs and GPUs simultaneously are presented using clusters equipped with varying node configurations.Comment: 20 pages, 12 figure

Real Time Wake Computations using Lattice Boltzmann Method on Many Integrated Core Processors

This paper puts forward an efficient Lattice Boltzmann method for use as a wake simulator suitable for real-time environments. The method is limited to low speed incompressible flow but is very efficient and can be used to compute flows “on the fly”. In particular, many-core machines allow for the method to be used with the need of very expensive parallel clusters. Results are shown here for flows around cylinders and simple ship shapes

Real Time Wake Computations using Lattice Boltzmann Method on Many Integrated Core Processors

This paper puts forward an efficient Lattice Boltzmann method for use as a wake simulator suitable for real-time environments. The method is limited to low speed incompressible flow but is very efficient and can be used to compute flows “on the fly”. In particular, many-core machines allow for the method to be used with the need of very expensive parallel clusters. Results are shown here for flows around cylinders and simple ship shapes