Julia: A Fresh Approach to Numerical Computing

@article{Bezanson2017JuliaAF,
  title={Julia: A Fresh Approach to Numerical Computing},
  author={Jeff Bezanson and Alan Edelman and Stefan Karpinski and Viral B. Shah},
  journal={ArXiv},
  year={2017},
  volume={abs/1411.1607}
}
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… 
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References

SHOWING 1-10 OF 53 REFERENCES
Computing in Operations Research Using Julia
TLDR
This paper explores how Julia, a modern programming language for numerical computing that claims to bridge this divide by incorporating recent advances in language and compiler design, can be used for implementing software and algorithms fundamental to the field of operations research, with a focus on mathematical optimization.
Julia: A Fast Dynamic Language for Technical Computing
TLDR
Julia is presented, a new dynamic language for technical computing, designed for performance from the beginning by adapting and extending modern programming language techniques, which enables an expressive programming model and successful type inference, leading to good performance for a wide range of programs.
Array Operators Using Multiple Dispatch: A design methodology for array implementations in dynamic languages
TLDR
This work has come to believe that while multiple dispatch has not been especially popular in most kinds of programming, technical computing is its killer application, and has developed an approach that yields a novel trade-off between flexibility and compile-time analysis.
Interactive Supercomputing’s Star-P Platform
TLDR
A classroom productivity study involving 29 students who have written a homework exercise in a low level language (MPI message passing) and a highlevel language (Star-P with MATLAB client), which indicates what perhaps should be of little surprise: the high level language is always far easier on the students than the lowlevel language.
Star-P: High Productivity Parallel Computing
TLDR
The focus of Star-P is to improve user productivity in parallel programming, and it is believed that it can dramatically reduce the difficulty of programming parallel computers by reducing the time needed for development and debugging.
Parallel programming and code selection in fortress
TLDR
This work discusses ideas for using a rich parameterized polymorphic type system to organize multithreading and data distribution on large parallel machines.
Parallel MATLAB: Doing it Right
TLDR
This work discusses the approaches the projects have taken to parallelize MATLAB, and describes innovative features in some of the parallel MATLAB projects, and gives an example of what it thinks is a "right" parallel MATLab.
Abstraction in technical computing
TLDR
Aion in Technical Computing by Jeffrey Werner Bezanson A.B., Harvard University (2004) S.M., Massachusetts Institute of Technology (2012) andsubmitted to the Department of Electrical Engineering and Computer Science in partial fulfillment of the requirements for the degree of Doctor of Philosophy.
X10: an object-oriented approach to non-uniform cluster computing
TLDR
A modern object-oriented programming language, X10, is designed for high performance, high productivity programming of NUCC systems and an overview of the X10 programming model and language, experience with the reference implementation, and results from some initial productivity comparisons between the X 10 and Java™ languages are presented.
Convex Optimization in Julia
TLDR
Convex translates problems from a user-friendly functional language into an abstract syntax tree describing the problem, which allows Convex to infer whether the problem complies with the rules of disciplined convex programming (DCP), and to pass the problem to a suitable solver.
...
1
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...