Background: R has become the de-facto reference analysis environment in Bioinformatics. Plenty of tools are available as packages that extend the R functionality, and many of them target the analysis of biological networks. Several algorithms for graphs, which are the most adopted mathematical representation of networks, are well-known examples of applications that require high-performance computing, and for which classic sequential implementations are becoming inappropriate. In this context, parallel approaches targeting GPU architectures are becoming pervasive to deal with the execution time constraints. Although R packages for parallel execution on GPUs are already available, none of them provides graph algorithms. Results: This work presents cuRnet, a R package that provides a parallel implementation for GPUs of the breath-rst search (BFS), the single-source shortest paths (SSSP), and the strongly connected components (SCC) algorithms. The package allows ooading computing intensive applications to GPU devices for massively parallel computation and to speed up the runtime up to one order of magnitude with respect to the standard sequential computations on CPU. We have tested cuRnet on a benchmark of large protein interaction networks and for the interpretation of high-throughput omics data thought network analysis. Conclusions: cuRnet is a R package to speed up graph traversal and analysis through parallel computation on GPUs. We show the eciency of cuRnet applied both to biological network analysis, which requires basic graph algorithms, and to complex existing procedures built upon such algorithms. Keywords: Graph traversal; GPU parallel implementation; Biological network analysis; High-throughput omics network annotation; Topological network analysis; Prize-collecting Steiner forest

cuRnet: an R package for graph traversing on GPU

Vincenzo Bonnici;Federico Busato;Stefano Aldegheri;Nicola Bombieri;Rosalba Giugno
2018

Abstract

Background: R has become the de-facto reference analysis environment in Bioinformatics. Plenty of tools are available as packages that extend the R functionality, and many of them target the analysis of biological networks. Several algorithms for graphs, which are the most adopted mathematical representation of networks, are well-known examples of applications that require high-performance computing, and for which classic sequential implementations are becoming inappropriate. In this context, parallel approaches targeting GPU architectures are becoming pervasive to deal with the execution time constraints. Although R packages for parallel execution on GPUs are already available, none of them provides graph algorithms. Results: This work presents cuRnet, a R package that provides a parallel implementation for GPUs of the breath-rst search (BFS), the single-source shortest paths (SSSP), and the strongly connected components (SCC) algorithms. The package allows ooading computing intensive applications to GPU devices for massively parallel computation and to speed up the runtime up to one order of magnitude with respect to the standard sequential computations on CPU. We have tested cuRnet on a benchmark of large protein interaction networks and for the interpretation of high-throughput omics data thought network analysis. Conclusions: cuRnet is a R package to speed up graph traversal and analysis through parallel computation on GPUs. We show the eciency of cuRnet applied both to biological network analysis, which requires basic graph algorithms, and to complex existing procedures built upon such algorithms. Keywords: Graph traversal; GPU parallel implementation; Biological network analysis; High-throughput omics network annotation; Topological network analysis; Prize-collecting Steiner forest
Algorithms
Bioinformatics
GPU
File in questo prodotto:
File Dimensione Formato  
s12859-018-2310-3.pdf

accesso aperto

Tipologia: Versione dell'editore
Licenza: Creative commons
Dimensione 3 MB
Formato Adobe PDF
3 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11562/979189
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 4
  • ???jsp.display-item.citation.isi??? 0
social impact