Publications

Authors:

Bonnici, Vincenzo; Busato, Federico; Aldegheri, Stefano; Akhmedov, Murodzhon; Cascione, Luciano; Arribas Carmena, Alberto; Bertoni, Francesco; Bombieri, Nicola; Kwee, Ivo; Giugno, Rosalba

Title:

cuRnet: an R package for graph traversing on GPU

Year:

2018

Type of item:

Articolo in Rivista

Tipologia ANVUR:

Articolo su rivista

Language:

Inglese

Referee:

No

Name of journal:

BMC BIOINFORMATICS

ISSN of journal:

1471-2105

N° Volume:

19

Page numbers:

1-10

Keyword:

Algorithms; Bioinformatics; GPU

Short description of contents:

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

Product ID:

102548

Handle IRIS:

11562/979189

Last Modified:

November 16, 2022

Bibliographic citation:

Bonnici, Vincenzo; Busato, Federico; Aldegheri, Stefano; Akhmedov, Murodzhon; Cascione, Luciano; Arribas Carmena, Alberto; Bertoni, Francesco; Bombieri, Nicola; Kwee, Ivo; Giugno, Rosalba, cuRnet: an R package for graph traversing on GPU «BMC BIOINFORMATICS» , vol. 19 , 2018 , pp. 1-10

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