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Julia: A Fresh Approach to Numerical Computing
Abstract
Bridging 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.
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Increasingly, circuit theory-based analyses are being conducted on larger and more complex datasets through recent advances in open-source software, programming, and cluster- or cloud-based computing resources.
“Increasingly, circuit theory-based analyses are being conducted on larger and more complex datasets through recent advances in open-source software (e.g., gflow; Leonard et al. 2017b), programming (e.g., the Julia numerical computing language; Bezanson et al. 2017), and cluster- or cloud-based computing resources.”
From Circuit‐theory applications to connectivity science and conservation (2019) · cites this paper as Bezanson et al. 2017
A long-term partnership to improve Circuitscape supported rapid growth in the tool's use and helped inspire the Julia programming language.
“Here, we describe how a long-term partnership to improve Circuitscape supported rapid growth in the tool's use, and also helped inspire a new high-performance programming language called Julia [32,33], which is now empowering the most recent Circuitscape release.”
From Circuitscape in julia: empowering dynamic approaches to connectivity assessment (2021) · cites this paper as [33]
Circuitscape was recently updated by migrating it from Python to the Julia programming language.
“We recently updated Circuitscape [34], a widely used, open-source connectivity analysis software package, by migrating it from Python [35] to the Julia programming language [33].”
From Circuitscape in julia: empowering dynamic approaches to connectivity assessment (2021) · cites this paper as [33]
Omniscape.jl is written in the Julia programming language to be fast, scalable, and easy-to-use.
“It is written in the Julia programming language (Bezanson et al., 2017) to be fast, scalable, and easy-to-use.”
From Omniscape.jl: software to compute omnidirectional landscape connectivity (2021) · cites this paper as Bezanson et al., 2017
The Julia programming language was first publicly released in 2012, is still in rapid development, and was in part inspired by Shah's experience collaborating with McRae to create a powerful yet accessible tool for biodiversity conservation applications.
“First publicly released in 2012, the Julia programming language is still in rapid development [32,33] and was, in part, inspired by Shah's experience collaborating with McRae to create a powerful yet accessible tool for biodiversity conservation applications.”
From Circuitscape in julia: empowering dynamic approaches to connectivity assessment (2021) · cites this paper as [33]
Often, a software tool may be first created in a high-productivity dynamically typed language, and as it matures and needs more performance and scale, it may be converted to a high-performance statically typed language.
“Often, a software tool may be first created in a high-productivity dynamically typed language, and as the tool matures and needs more performance and scale (ability to maintain performance as the problem size increases), it may be converted to a highperformance statically typed language [32,33].”
From Circuitscape in julia: empowering dynamic approaches to connectivity assessment (2021) · cites this paper as [33]
In general, the more information the system has about a program's data inputs, the faster a program can run.
“In general, the more information the system has about a program's data inputs, the faster a program can run.”
From Circuitscape in julia: empowering dynamic approaches to connectivity assessment (2021) · cites this paper as [33]
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Metadata
- Authors
- Bezanson, J., Edelman, A., Karpinski, S., & Shah, V. B.
- Venue
- SIAM Review
- Published
- 2017-01
- Volume
- 59
- Issue
- 1
- Pages
- 65-98
- ISSN
- 0036-1445
- 1095-7200
- Cited by
- 5,440
- References
- 0
- Language
- en
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