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Julia: A Fast Dynamic Language for Technical Computing

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Julia is a high-level, high-performance dynamic language carefully designed to be excellent for technical computing. It has been gaining traction as a an alternative to Matlab, R and NumPy – especially in performance-critical areas such as machine learning, “big statistics”, linear algebra and image analysis. One important feature that distinguishes Julia from other systems – even general purpose languages like C or Scheme – is that it doesn’t special-case numerics at all. Basic types like integers and floating-point numbers are actually user-defined, with layout and behavior defined in Julia – they just happen to be defined before your program starts. As a corollary, your custom data types – be they exotic kinds of numbers or standard data structures – will be just as fast and efficient as Julia’s builtins. We’ll explore the language design choices and implementation techniques that enable this, including pervasive dynamic multiple dispatch, run-time code generation with aggressive specialization, and dataflow-based type inference. There will be lots of live coding and demos: from creating custom numeric types to analyzing and visualizing data. The talk will wrap up with a discussion of how we’re pushing the envelope of open source by making it unprecedentedly easy for people to contribute to the language itself, its packages, and its ecosystem.

This talk is part of the Computer Laboratory Digital Technology Group (DTG) Meetings series.

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