cuRnet: an R package for graph traversing on GPU

• Published in: BMC bioinformatics Vol. 19; no. Suppl 10; pp. 356 - 24
• Main Authors: Bonnici, Vincenzo, Busato, Federico, Aldegheri, Stefano, Akhmedov, Murodzhon, Cascione, Luciano, Carmena, Alberto Arribas, Bertoni, Francesco, Bombieri, Nicola, Kwee, Ivo, Giugno, Rosalba
• Format: Journal Article
• Language: English
• Published: London BioMed Central 15.10.2018; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Algorithms; Bioinformatics; Biological analysis; Biological network analysis; Biomedical and Life Sciences; C plus plus; Cell cycle; Computational biology; Computational Biology - methods; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Computer Graphics; Computing Methodologies; Data processing; Gene expression; GPU parallel implementation; Graph traversal; Graphical representations; High-throughput omics network annotation; Life Sciences; Lymphoma; Microarrays; Network analysis; Networks; Packages; Parallel processing; Prize-collecting Steiner forest; Prostate cancer; Proteins; Topological network analysis; High-throughput omics network annotation; Topological network analysis; Biological network analysis; GPU parallel implementation; Graph traversal; Prize-collecting Steiner forest
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-018-2310-3

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-first search (BFS), the single-source shortest paths (SSSP), and the strongly connected components (SCC) algorithms. The package allows offloading 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 efficiency of cuRnet applied both to biological network analysis, which requires basic graph algorithms, and to complex existing procedures built upon such algorithms.