View source for Numerical Codes on Multi-GPU Architectures

Many real world applications involving numerical algorithms require
high performance computing (HPC), either because of time constraints like
for real-time simulations or because a large amount of data
has to be processed.

GPUs offer high computational performance at relatively low cost and are
therefore an interesting architecture especially for data parallel
applications. Recently, more and more GPU HPC clusters
arise and thus there is a need for adapting numerical codes to
Multi-GPU environments. In this talk we consider two
common numerical algorithms on GPU clusters, namely multigrid and
the Lattice Boltzmann Method (LBM).
Multigrid is among the most efficient numerical solvers for a variety
of large, sparse (linear) systems arising e.g. from discretized
PDEs. LBM is used in computational fluid dynamics (CFD) and follows in
contrast to the Navier-Stokes equations a microscopic CFD approach based
on cellular automata and kinetic theory.

We introduce GPU programming paradigms, compare CUDA and OpenCL,
and show performance results for exemplary applications in CFD and imaging
on different GPU platforms and clusters.