New journal paper: Energy-aware acceleration on GPUs: Findings on a bioinformatics benchmark

The work Energy-aware acceleration on GPUs: Findings on a bioinformatics benchmark has been published in Sustainable Computing: Informatics and Systems vol. 20, pp. 88 – 101.

Abstract:

This paper performs a complete study on performance and energy efficiency of biomedical codes when accelerated on GPUs (Graphics Processing Units). We have selected a benchmark composed of three different building blocks which constitute the pillars of four popular biomedical applications: Q-norm, for the quantile normalization of gene expressions, reg f3d, for the registration of 3D images within the NiftyReg library, bedpostx (from the FSL neuroimaging package) and a multi-tensor tractography for the analysis of diffusion images. We try to identify (1) potential scenarios where performance per watt can be optimal in large-scale biomedical applications, and (2) the ideal GPU platform among a wide range of models, including low power Tegras, popular GeForces and high-end Titans. Experimental results conclude that data locality and arithmetic intensity represent the most rewarding ways on the road to high performance bioinformatics when power is a major concern.

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