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In our former works we have made serious efforts to improve the performance of medical image analysis methods with using ensemble-based systems. In this paper, we present a novel hardware-based solution for the efficient adoption of our complex, fusion-based approaches for real-time applications. Even though most of the image processing problems and the increasing amount of data have high-performance computing (HPC) demand, there is still a lack of corresponding dedicated HPC solutions for several medical tasks. To widen this bottleneck we have developed a Hybrid Small Size high performance computing Resource (abbreviated by HuSSaR) which efficiently alloys CPU and GPU technologies, mobile and has an own cooling system to support easy mobility and wide applicability. Besides a proper technical description, we include several practical examples from the clinical data processing domain in this work. For more details see also: https://arato.inf.unideb. hu/kovacs.laszlo/research_hybridmicrohpc.html

In our contribution we evaluate the energy consumption optimization of image rendering on a typical architecture of an HPC system. We use the renderer CyclesPhi, which is our own modified version of the Cycles renderer from the Blender 3D creation suite. CyclesPhi fits the HPC environment in such a way that it runs as a client on one or multiple nodes, and efficiently utilizes the cluster through optimal load balancing. In order to reduce the energy consumption of a scene rendering we used MERIC, our own developed library for HPC application profiling and runtime tuning. MERIC searches for the configuration of hardware, system software, and application parameters which can provide minimal energy consumption for each manually instrumented region inside the analysed application. Thusly we instrumented the Blender client and analysed the rendering task. On Haswell architecture (two Intel Xeon E5-2680v3 processors per node) we were able to reduce energy consumption by 9% while extending the rendering time by 21%. If a less energy conservative setting was applied, we would save 4.8% of energy whilest only prolonging the rendering time by 4%.

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