Energy-aware acceleration on GPUs: Findings on a bioinformatics benchmark
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.
Citation
Please, cite this work as:
[Pér+18] J. Pérez, A. Rodríguez, J. F. Chico, et al. “Energy-aware acceleration on GPUs: findings on a bioinformatics benchmark”. In: Sustainable Computing: Informatics and Systems 20 (2018), pp. 88-101. DOI: 10.1016/j.suscom.2018.01.001.
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[1] P. Teikari, R. P. Najjar, L. Schmetterer, et al. “Embedded deep learning in ophthalmology: making ophthalmic imaging smarter”. In: Therapeutic Advances in Ophthalmology 11 (Jan. 2019). ISSN: 2515-8414. DOI: 10.1177/2515841419827172. URL: http://dx.doi.org/10.1177/2515841419827172.