ConclusionsĬuRnet is a R package to speed up graph traversal and analysis through parallel computation on GPUs. We have tested cuRnet on a benchmark of large protein interaction networks and for the interpretation of high-throughput omics data thought network analysis.
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. 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. Although R packages for parallel execution on GPUs are already available, none of them provides graph algorithms. In this context, parallel approaches targeting GPU architectures are becoming pervasive to deal with the execution time constraints. 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. Plenty of tools are available as packages that extend the R functionality, and many of them target the analysis of biological networks. R has become the de-facto reference analysis environment in Bioinformatics.
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