high performance computing on graphics processing units: hgpu.org

A branch of Biomedical image processing involves analyzing images containing elongates structures. The enhancement of these structures in noisy image data is often required to enable automatic image analysis. A framework for such noise reduction based on Coherence Enhancing Diffusion (CED) using Orientation Scores (OS) has been developed. However, owing to the high computational complexity and high memory consumption of this approach, the current implementation is not able to process sizeable images in reasonable time. This paper presents a GPU/CPU-based optimization of the OSCED framework. The primary goal of this work was to reduce the execution time of the framework by harnessing the processing capabilities offered by an existing GPU cluster. First, the bottlenecks were identified. These were subsequently improved by applying a number of CPU- and GPU-based optimizations. Using a set of reference images, we show that the performance of the framework improved by at least an order of magnitude following our optimizations. In addition, we present a ‘split-and-merge’ approach and illustrate its potential for further performance improvement using the existing GPU cluster as a reference. We conclude that significant performance gains can be obtained by applying our approach to a suitable cluster configuration. Furthermore, there is still room for optimizing the parts that are currently executed on the CPU.