Graphics processing units in bioinformatics, computational biology and systems biology

Marco Nobile, Paolo Cazzaniga, Andrea Tangherloni, Daniela Besozzi

Research output: Contribution to journalReview articleAcademicpeer-review

65 Citations (Scopus)
35 Downloads (Pure)

Abstract

Several studies in Bioinformatics, Computational Biology and Systems Biology rely on the definition of physico-chemical or mathematical models of biological systems at different scales and levels of complexity, ranging from the interaction of atoms in single molecules up to genome-wide interaction networks. Traditional computational methods and software tools developed in these research fields share a common trait: they can be computationally demanding on Central Processing Units (CPUs), therefore limiting their applicability in many circumstances. To overcome this issue, general-purpose Graphics Processing Units (GPUs) are gaining an increasing attention by the scientific community, as they can considerably reduce the running time required by standard CPU-based software, and allow more intensive investigations of biological systems. In this review, we present a collection of GPU tools recently developed to perform computational analyses in life science disciplines, emphasizing the advantages and the drawbacks in the use of these parallel architectures.
Original languageEnglish
Pages (from-to)870-885
Number of pages16
JournalBriefings in Bioinformatics
Volume18
Issue number5
DOIs
Publication statusPublished - 1 Sep 2017
Externally publishedYes

Keywords

  • CUDA
  • Bioinformatics
  • Computational biology
  • Systems biology
  • Graphics processing units
  • High performance computing
  • High-performance computing
  • Computer Graphics
  • Algorithms
  • Software
  • Systems Biology

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