high performance computing on graphics processing units: hgpu.org

We demonstrate that for modern medical imaging applications, parallel implementations on traditional parallel architectures (clusters and multiprocessor servers) can be outperformed, both in terms of speed and cost-effectiveness, by new implementations on next-generation architectures like GPUs (Graphics Processing Units). Although, compared to clusters and multiprocessor servers, GPUs are rather small and much less expensive, they consist of several SIMD-processors and thus provide a high degree of parallelism. For an iterative image reconstruction algorithm—the list-mode OSEM— we demonstrate, first, the limitations of parallel reconstructions with this algorithm on the traditional parallel architectures, and second, how the well-analyzed parallel strategies for traditional architectures can be adapted systematically to achieve fast reconstructions on the GPU.