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
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.
BibTeX
<pre><code><button class='copy-bib-btn' id='copy-bib-btn'><i class='bi bi-clipboard'></i> Copy BibTeX</button>Bibliometric data
The following data has been extracted from resources such as OpenAlex, Dimensions, PlumX or Altmetric.
Cites
The following graph plots the number of cites received by this work from its publication, on a yearly basis.
Papers citing this work
The following is a non-exhaustive list of papers that cite this work:
- Sergio Santander‐Jiménez, Miguel Á. Vega-Rodríguez (2025). Multi-threaded SIMD strategies and frequency scaling for sustainable evolutionary biology. Cluster Computing DOI
- Petteri Teikari, Raymond P. Najjar, Leopold Schmetterer, et al. (2019). Embedded deep learning in ophthalmology: making ophthalmic imaging smarter. Therapeutic Advances in Ophthalmology DOI