A Performance/Cost Evaluation for a GPU-Based Drug Discovery Application on Volunteer Computing

Abstract

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) has 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 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). This paper explores 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 that 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.

Big Data Mining Using Public Distributed Computing

“…Volunteer or crowd computing is a popular method for solving complex research problems from an increasingly diverse range of areas [41]. It permits the user to forget about certain costs associated with buying and maintaining physical infrastructures and helps disseminate the project to the public [25]. Establishing an own specialized center for data processing might be too expensive or inappropriate.…”

“…The most popular and standard software for volunteer computing projects is called BOINC (Berkeley Open Infrastructure for Network Computing) [4,25]. BOINC is designed for volunteer and grid computing.…”

“…It allows to acquire the hardware from multiple users, thus diverging the costs. This is especially significant if GPUs are used for computations, which may raise the power requirements up to 30% per each instance [25]. Current high-end GPUs have high energy consumption rates, making it a big issue having large clusters.…”

“…There are cost estimation models for local GPUbased infrastructures using equations for calculating computation costs per each instance [25]. The equations may consider energy consumption costs, machine market price and machine collocation costs.…”

Computational Approaches for Drug Discovery

“…General‐purpose graphics processing units (GPGPUs) have been applied to solve computation‐intensive problems of a variety of scientific domains [Guerrero et al., ]. GPGPUs have grown from a few cores to over a hundred and has significantly enhanced computational power.…”