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A performance/cost evaluation for a GPU-based drug discovery application on volunteer computing

Gines David Guerrero Hernandez, Baldomero Imbernon, Horacio Perez-Sanchez, Francisco Sanz, Jose M. Garcia, Jose M. Cecilia
National Laboratory for High Performance Computing, Center of Mathematical Modeling – University of Chile
BioMed Research International, 2014

@article{hernandez2014performance,

   title={A performance/cost evaluation for a GPU-based drug discovery application on volunteer computing},

   author={Hernandez, Gines David Guerrero and Imbernon, Baldomero and Perez-Sanchez, Horacio and Sanz, Francisco and Garcia, Jose M. and Cecilia, Jose M.},

   year={2014}

}

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Bioinformatics is an interdisciplinary research field that develops tools for the analysis of large biological databases, and thus the use of high-performance computing (HPC) platforms is mandatory for the generation of useful biological knowledge. The latest generation of graphics processing units (GPUs) have democratized the use of HPC as they push desktop computers to cluster-level performance. Many applications within this field have been developed to leverage these powerful and apparently low-cost architectures. However, these applications still need to scale to larger GPU-based systems to enable remarkable advances in the fields of healthcare, drug discovery, genome research, etc. The inclusion of GPUs in HPC systems exacerbates power and temperature issues, increasing the Total Cost of Ownership (TCO), and thus making unfeasible to have those systems for small institutions. In this paper, we explore the benefits of volunteer computing to scale bioinformatics applications as an alternative to own large GPU-based local infrastructures. We use as a benchmark a GPU-based drug discovery application called BINDSURF, which is within those applications that have been designed to leverage GPU computing, but their computational requirements go beyond a single desktop machine. Volunteer computing is presented as a cheap and valid HPC system for those Bioinformatics applications that need to process huge amounts of data and where the response time is not a critical factor.
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