ArticleBioinformatics (Oxford, England)2010
The gputools package enables GPU computing in R.
Article in Bioinformatics (Oxford, England), 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed.
- High-Performance Statistical Computing in the Computing Environments of the 2020s.Statistical science : a review journal of the Institute of Mathematical Statistics · 2022Article
- Big Data and Neuroimaging.Statistics in biosciences · 2017Article
- FamSeq: a variant calling program for family-based sequencing data using graphics processing units.PLoS computational biology · 2014Article
- Article
- Parallel implementation of 3D protein structure similarity searches using a GPU and the CUDA.Journal of molecular modeling · 2014Article
- Discovering epistasis in large scale genetic association studies by exploiting graphics cards.Frontiers in genetics · 2013Review
- GPUmotif: an ultra-fast and energy-efficient motif analysis program using graphics processing units.PloS one · 2012Article
- Statistical considerations for analysis of microarray experiments.Clinical and translational science · 2011Review
- Epistasis detection on quantitative phenotypes by exhaustive enumeration using GPUs.Bioinformatics (Oxford, England) · 2011Article
- Fast network centrality analysis using GPUs.BMC bioinformatics · 2011Article
- EPIBLASTER-fast exhaustive two-locus epistasis detection strategy using graphical processing units.European journal of human genetics : EJHG · 2011Article
- Fast and accurate protein substructure searching with simulated annealing and GPUs.BMC bioinformatics · 2010Article
- Graphics Processing Units and High-Dimensional Optimization.Statistical science : a review journal of the Institute of Mathematical Statistics · 2010Article
- permGPU: Using graphics processing units in RNA microarray association studies.BMC bioinformatics · 2010Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
motivationBy default, the R statistical environment does not make use of parallelism. Researchers may resort to expensive solutions such as cluster hardware for large analysis tasks. Graphics processing units (GPUs) provide an inexpensive and computationally powerful alternative. Using R and the CUDA toolkit from Nvidia, we have implemented several functions commonly used in microarray gene expression analysis for GPU-equipped computers.
resultsR users can take advantage of the better performance provided by an Nvidia GPU. AVAILABILITY: The package is available from CRAN, the R project's repository of packages, at http://cran.r-project.org/web/packages/gputools More information about our gputools R package is available at http://brainarray.mbni.med.umich.edu/brainarray/Rgpgpu
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.