GNU Parallel: The Command-Line Power Tool

By Ole Tange. 2011

1,311 citations recorded by the source corpus.

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Citations in the corpus

UNICORN on RAINBOW: A Universal Commonsense Reasoning Model on a New Multitask Benchmark (2021, methodology): “Training times usually took several hours per run, so we ran many experiments in parallel on the VMs using GNU Parallel (Tange 2011).”
Optimal Decision Trees for the Algorithm Selection Problem: Integer Programming Based Approaches (2019, methodology): “Tasks where scheduled in parallel (7 threads simultaneously) with the GNU Parallel package (Tange, 2011).”
Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks (2014, methodology): “For parallelization, we used the convenient command line tool GNU parallel (Tange, 2011).”
A FORMAL PROOF OF THE KEPLER CONJECTURE (2015, neutral): “all verifications were made in parallel with 32 cores using GN U parallel [46].”
Prokka: rapid prokaryotic genome annotation (2014, positive): “However, Prokka is more efficient if it runs multiple single CPU threads on subsets of the data, which it achieves using GNU parallel (Tange, 2011).”
De novo assembly of the Aedes aegypti genome using Hi-C yields chromosome-length scaffolds (2016, methodology): “) It is optimized for speed using GNU Parallel shell tool (27), but can be run without parallelization.”
HPEPDOCK: a web server for blind peptide–protein docking based on a hierarchical algorithm (2018, neutral): “For global peptide docking, four CPU cores are assigned to a job parallel running (40), while for local peptide docking, one CPU core is assigned to a job.”
Trespassing the Boundaries: Labeling Temporal Bounds for Object Interactions in Egocentric Video (2017, background): “For conventional annotations, we report an average IoU = 0.63 and a standard deviation of 0.22, whereas for RB annotations we report increased average IoU = 0.83 with a lower standard deviation of 0.11.”
Denoising the Denoisers: an independent evaluation of microbiome sequence error-correction approaches (2018, methodology): “114 In summary, primers were trimmed off all reads using Cutadapt (v 1.14) (Martin, 2011) and 115 GNU Parallel (Tange, 2011).”
Improving robustness against common corruptions by covariate shift adaptation (2020, background): “, 2020), GNU parallel (Tange, 2011), Tensorflow (Abadi et al.”
Many-body localization and delocalization in large quantum chains (2018, methodology): “We used Matplotlib [80] to generate figures and GNU Parallel [81] for running parallel simulations.”