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Julia: A fresh approach to technical computing and data science
Submitted by Viral B. Shah (@viralbshah) on Monday, 1 April 2013
Analytics and Visualization
Julia is a new high performance, open source, dynamic language for technical computing and especially relevant for the upcoming field of data science. I will describe the rationale and the vision behind julia, key language features, and show some demos so that attendees can get a feel for the language. I will also discuss Julia's open source development process and the community that keeps adding amazing contributions at a rapid pace. Having done all my Julia work out of Bangalore, one of my personal objectives is also to grow the local Julia user and developer community.
Julia provides a sophisticated compiler, distributed parallel execution, numerical accuracy, and an extensive mathematical function library. The library, largely written in Julia itself, also integrates mature, best-of-breed C and Fortran libraries for linear algebra, random number generation, signal processing, and string processing. Performance of Julia programs is often within a factor of two of C programs and in many cases as good as good as C. This obviates the need to write computational kernels in C or Fortran and leads to higher programmer productivity. The base julia repository has received contributions from over 135 contributors. In addition, the Julia developer community has contributed over 125 external packages through Julia’s built-in package manager.
I am one of the co-creators of the Julia programming language, along with Jeff Bezanson, Stefan Karpinski, and Alan Edelman.
My LinkedIn profile is provided in the links below.