9. GPU Best Practices for HPC Applications at Industry Scale

Current trends in high performance computing (HPC) are moving towards the availability of several cores on the same chip of contemporary processors in order to achieve speed-up through exploiting the potential of fine-grain parallelism in applications. The trend is led by graphics processing units (GPUs) which have recently been developed exclusively for computational tasks as massively-parallel co-processors to conventional x86 CPUs. Since the introduction in 2006 of the NVIDIA Tesla GPU and CUDA programming environment, the HPC community has achieved noted performance gains across a broad range of application software. In particular, various scientific research disciplines within computational physics and chemistry have reported performance levels as high as two orders of magnitude over current quad-core CPUs. During 2010 an extensive set of new HPC architectural features were offered in the third generation Tesla and CUDA (codenamed Fermi), giving engineering disciplines a similar opportunity to expand use of GPUs for applications relevant to industry modeling and simulation. Similar to the scientific research community, practical applications in industry observe constant growth in model fidelity, but parallel efficiency of commercial software and job completion times also become important factors behind decisions on model size and scale, and level of physics features to include. This work examines algorithmic development best practices, and performance results of application software for the Tesla Fermi architecture in modelling and simulation examples relevant to industry-scale HPC practice. Included are GPU implementations of computational structural mechanics (CSM) and computational fluid dynamics (CFD) software that support mechanical product design in manufacturing industries. Specifically, the critical requirements of memory optimization and storage formats are discussed for grid-based direct solvers that appear in CSM and for highly irregular sparse matrices that require iterative solver schemes in CFD.