Design space exploration towards a realtime and energy-aware GPGPU-based analysis of biosensor data
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In this paper, novel objectives for the design space exploration of GPGPU applications are presented. The design space exploration takes the combination of energy efficiency and realtime requirements into account. This is completely different to the commonest high performance computing objective, which is to accelerate an application as much as possible.
As a proof-of-concept, a GPGPU based image processing and virus detection pipeline for a newly developed biosensor, called PAMONO, is presented. The importance of realtime capable and portable biosensors increases according to rising number of worldwide spreading virus infections. The local availability of biosensors at e.g. airports to detect viruses in-situ demand to take costs and energy for the development of GPGPU-based biosensors into account. The consideration of the energy is especially important with respect to green computing.
The results of the conducted design space exploration show that during the design process of a GPGPU-based application the platform must also be evaluated to get the most energy-aware solution. In particular, it was shown that increasing numbers of parallel running cores need not decrease the energy consumption.
KeywordsGPGPU Design space exploration Energy awareness
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