high performance computing on graphics processing units: hgpu.org

We present a variety of possible parallelization approaches for a real-world case study using several modern parallel and distributed computer architectures. Our case study is a production-quality, time-intensive algorithm for medical image reconstruction used in computer tomography. We describe how this algorithm can be parallelized for the main kinds of contemporary parallel architectures: shared-memory multiprocessors, distributed-memory clusters, graphics processors, the Cell processor and, finally, how various architectures can be accessed in a distributed Grid environment. The main contribution of the paper, besides the parallelization approaches, is their systematic comparison regarding four important criteria: performance, programming comfort, accessibility, and cost-effectiveness. We report results of experiments on particular parallel machines of different architectures that confirm the findings of our systematic comparison.