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An increasingly large number of HPC systems rely on heterogeneous architectures combining traditional multi-core CPUs with power  efficient accelerators.\udDesigning efficient applications for these systems has been troublesome in the past as accelerators could usually be programmed only using specific programming languages -- such as CUDA -- threatening maintainability, portability and correctness.\ud\udSeveral new programming environments try to tackle this problem; among them OpenACC offers a high-level approach based on directives.  In OpenACC, one annotates existing C, C++ or Fortran codes with compiler directive clauses to mark program regions to offload and run on accelerators and to identify available parallelism. \ud\udThis approach directly addresses code portability, leaving to compilers the support of each different accelerator, but one has to carefully assess the relative costs of potentially portable approach versus computing efficiency. In this paper we address precisely this issue, using as a test-bench a massively parallel Lattice Boltzmann code.\ud\udWe implement and optimize this multi-node code using OpenACC and OpenMPI. We also compare performance with that of the same algorithm written in CUDA, OpenCL and C for GPUs, Xeon-Phi and traditional multi-core CPUs, and characterize through an accurate time model its scaling behavior on a large cluster of GPUs

Accelerating Lattice Boltzmann Applications with OpenACC