Jug: Software for Parallel Reproducible Computation in Python

Software Paper, Article English OPEN
Luis Pedro Coelho;
(2017)
  • Publisher: Ubiquity Press
  • Journal: Journal of Open Research Software (issn: 2049-9647, eissn: 2049-9647)
  • Publisher copyright policies & self-archiving
  • Related identifiers: doi: 10.5334/jors.161
  • Subject: Memoization | Python | Computational science | Data analysis | Computer software | Parallel programming; Python; Memoization; Reproducible computation; High performance computing; Data analysis; Computational science | Reproducible computation | High performance computing | Parallel programming | QA76.75-76.765

As computational pipelines become a bigger part of science, it is important to ensure that the results are reproducible, a concern which has come to the fore in recent years. All developed software should be able to be run automatically without any user intervention. In... View more
  • References (19)
    19 references, page 1 of 2

    1. Altintas, I, Berkley, C, Jaeger, E, Jones, M, Ludascher, B and Mock, S 2004 “Kepler: an extensible system for design and execution of scientific workflows”. In: Scientific and Statistical Database Management , Proceedings. 16th International Conference on (Apr. 2004) URL: https://scholar.google.com/scholar?clust er=17284613261601846997 (cit. on p.).

    2. Augustin, S and Müller, C 2013 “Interference effects in Bethe-Heitler pair creation in a bichromatic laser field”. Physical Review A, 88(2): 022109. ISSN: 2469- 9934. (cit. on p.). DOI: https://doi.org/10.1103/ PhysRevA.88.022109

    3. Baumer, B, Cetinkaya-Rundel, M, Bray, A, Loi, L and Horton, N J 2014 “R Markdown: Integrating A Reproducible Analysis Tool into Introductory Statistics”. Technological Innovations in Statistics Education, 8. (cit. on p.).

    4. Beazley, D M “Automated scientific software scripting with SWIG”. In: Future Generation Computer Systems, 19 (Mar. 2003). URL: https://scholar.google.com/scho lar?cluster=14166776132178739884 (cit. on p.). DOI: https://doi.org/10.1016/S0167-739X(02)00171-1 5. Beazley, D M 1996 “SWIG: An Easy to Use Tool for Integrating Scripting Languages with C and C++.” In: Tcl/Tk Workshop. URL: https://scholar.google.com/sc holar?cluster=2768773569829356266 (cit. on p.). In: The Journal of Open Source Software, 1(8): (Dec. 2016). (cit. on p.). DOI: https://doi.org/10.21105/ joss.00107

    51. Sadedin, S P, Pope, B and Oshlack, A “Bpipe: a tool for running and managing bioinformatics pipelines”. In: Bioinformatics, 28(11): 1525-1526. (Dec. 2012). ISSN: 1367-4803. (cit. on p.). DOI: https://doi.org/10.1093/ bioinformatics/bts167

    52. Saul, A D, Hensman, J, Vehtari, A and Lawrence, N D 2016 “Chained Gaussian Processes”. In: BMC Bioinformatics, 14(1): 1431-1440. ISSN: 1471-2105. (cit. on p.). DOI: https://doi.org/10.1186/1471-2105- 14-252

    53. Schwab, M, Karrenbach, M and Claerbout, J 2000 “Making scientific computations reproducible”. In: Computing in Science & Engineering, 2(6): 61-67. ISSN: 1521-9615. (cit. on p.). DOI: https://doi. org/10.1109/5992.881708

    54. Severin, J, Beal, K, Vilella, A, Fitzgerald, S, Schuster, M, Gordon, L, Ureta-Vidal, A, Flicek, P and Herrero, J “eHive: An Artificial Intelligence workflow system for genomic analysis”. In: BMC Bioinformatics, 11(1): 240. ISSN: 1471-2105. (cit. on p.). URL: http://www.biomedcentral.com/1471- 2105/11/240 (Oct. 2010).

    55. Sorge, A pyfssa 0.7.6. Dec. 2015. (cit. on p.). DOI: https://doi.org/10.5281/zenodo.35293

    56. Spjuth, O, Bongcam-Rudloff, E, Hernández, G C, Forer, L, Giovacchini, M, Guimera, R V, Kallio, A, Korpelainen, E, Kańduła, M M, Krachunov, M, Kreil, D P, Kulev, O, Łabaj, P P, Lampa, S, Pireddu, L, Schönherr, S, Siretskiy, A and Vassilev, D 2015 “Experiences with workflows for automating dataintensive bioinformatics”. In: Biology Direct, 10(1). ISSN: 1745-6150. (cit. on p.). DOI: https://doi.org/10.1186/ s13062-015-0071-8

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