Frameworks for GPU Accelerators: A comprehensive evaluation using 2D/3D image registration

Abstract

In the last decade, there has been a dramatic growth in research and development of massively parallel many-core architectures like graphics hardware, both in academia and industry. This changed also the way programs are written in order to leverage the processing power of a multitude of cores on the same hardware. In the beginning, programmers had to use special graphics programming interfaces to express general purpose computations on graphics hardware. Today, several frameworks exist to relieve the programmer from such tasks. In this paper, we present five frameworks for parallelization on GPU Accelerators, namely RapidMind, PGI Accelerator, HMPP Workbench, OpenCL, and CUDA. To evaluate these frameworks, a real world application from medical imaging is investigated, the 2D/3D image registration.