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High performance computing increasingly relies on heterogeneous systems, based on multi-core CPUs, tightly coupled to accelerators: GPUs or many core systems. Programming heterogeneous systems raises new issues: reaching high sustained performances means that one must exploit parallelism at several levels; at the same time the lack of a standard programming environment has an impact on code portability. This paper presents a performance assessment of a {\em massively parallel} and {\em portable} Lattice Boltzmann code, based on the Open Computing Language (OpenCL) and the Message Passing Interface (MPI). Exactly the same code runs on standard clusters of multi-core CPUs, as well as on hybrid clusters including accelerators. We consider a state-of-the-art Lattice Boltzmann model that accurately reproduces the thermo-hydrodynamics of a fluid in 2 dimensions. \ud\udThis algorithm has a regular structure suitable for accelerator architectures with a large degree of parallelism, but it is not straightforward to obtain  a large fraction of the theoretically available performance. \ud\udIn this work we focus on portability of code across several heterogeneous architectures preserving performances and also on techniques to move data between accelerators minimizing overheads of communication latencies. \ud\udWe describe the organization of the code and present and analyze performance and scalability results on a cluster of nodes based on NVIDIA K20 GPUs and Intel Xeon-Phi accelerators

On Portability, Performance and Scalability of an MPI OpenCL Lattice Boltzmann Code