Scalable lattice Boltzmann solvers for CUDA GPU clusters

• An MPI-CUDA implementation of the lattice Boltzmann method is described.
• We propose a method to handle efficiently 3D partitions of the simulation domain.
• We study the performance of our implementation on a cluster using up to 24 GPUs.
• Peak performance as well as weak and strong scalability are satisfactory.

The lattice Boltzmann method (LBM) is an innovative and promising approach in computational fluid dynamics. From an algorithmic standpoint it reduces to a regular data parallel procedure and is therefore well-suited to high performance computations. Numerous works report efficient implementations of the LBM for the GPU, but very few mention multi-GPU versions and even fewer GPU cluster implementations. Yet, to be of practical interest, GPU LBM solvers need to be able to perform large scale simulations. In the present contribution, we describe an efficient LBM implementation for CUDA GPU clusters. Our solver consists of a set of MPI communication routines and a CUDA kernel specifically designed to handle three-dimensional partitioning of the computation domain. Performance measurement were carried out on a small cluster. We show that the results are satisfying, both in terms of data throughput and parallelisation efficiency.