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dc.contributor.authorKarpinski, Stefan
dc.contributor.authorShah, Viral B.
dc.contributor.authorBezanson, Jeffrey Werner
dc.contributor.authorEdelman, Alan
dc.date.accessioned2017-06-21T15:54:49Z
dc.date.available2017-06-21T15:54:49Z
dc.date.issued2017-02
dc.date.submitted2014-12
dc.identifier.issn0036-1445
dc.identifier.issn1095-7200
dc.identifier.urihttp://hdl.handle.net/1721.1/110125
dc.description.abstractBridging cultures that have often been distant, Julia combines expertise from the diverse fields of computer science and computational science to create a new approach to numerical computing. Julia is designed to be easy and fast and questions notions generally held to be “laws of nature" by practitioners of numerical computing: \beginlist \item High-level dynamic programs have to be slow. \item One must prototype in one language and then rewrite in another language for speed or deployment. \item There are parts of a system appropriate for the programmer, and other parts that are best left untouched as they have been built by the experts. \endlist We introduce the Julia programming language and its design---a dance between specialization and abstraction. Specialization allows for custom treatment. Multiple dispatch, a technique from computer science, picks the right algorithm for the right circumstance. Abstraction, which is what good computation is really about, recognizes what remains the same after differences are stripped away. Abstractions in mathematics are captured as code through another technique from computer science, generic programming. Julia shows that one can achieve machine performance without sacrificing human convenience.en_US
dc.description.sponsorshipNational Science Foundation (U.S.) (CCF-0832997)en_US
dc.description.sponsorshipNational Science Foundation (U.S.) (DMS-1016125)en_US
dc.description.sponsorshipNational Science Foundation (U.S.) (DMS-1312831)en_US
dc.language.isoen_US
dc.publisherSociety for Industrial and Applied Mathematicsen_US
dc.relation.isversionofhttp://dx.doi.org/10.1137/141000671en_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.sourceSIAMen_US
dc.titleJulia: A Fresh Approach to Numerical Computingen_US
dc.typeArticleen_US
dc.identifier.citationBezanson, Jeff; Edelman, Alan; Karpinski, Stefan and Shah, Viral B. “Julia: A Fresh Approach to Numerical Computing.” SIAM Review 59, no. 1 (January 2017): 65–98 © 2017 Society for Industrial and Applied Mathematicsen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mathematicsen_US
dc.contributor.mitauthorBezanson, Jeffrey Werner
dc.contributor.mitauthorEdelman, Alan
dc.relation.journalSIAM Reviewen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsBezanson, Jeff; Edelman, Alan; Karpinski, Stefan; Shah, Viral B.en_US
dspace.embargo.termsNen_US
dc.identifier.orcidhttps://orcid.org/0000-0001-7676-3133
mit.licensePUBLISHER_POLICYen_US


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