high performance computing on graphics processing units: hgpu.org

The development of standard processors changed in the last years moving from bigger, more complex, and faster cores to putting several more simple cores onto one chip. This changed also the way programs are written in order to leverage the processing power of multiple cores of the same processor. In the beginning, programmers had to divide and distribute the work by hand to the available cores and to manage threads in order to use more than one core. Today, several frameworks exist to relieve the programmer from such tasks. In this paper, we present five such frameworks for parallelization on shared memory multi-core architectures, namely OpenMP, Cilk++, Threading Building Blocks, RapidMind, and OpenCL. To evaluate these frameworks, a real world application from medical imaging is investigated, the 2D/3D image registration. In an empirical study, a fine-grained data parallel and a coarse-grained task parallel parallelization approach are used to evaluate and estimate different aspects like usability, performance, and overhead of each framework.