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Julia: A Fast Dynamic Language for Technical Computing
--client
enthought
--show
scipy_2012
--room Ballroom_CDE 1175 --force
Next: 11 Performance Python Panel Discussion
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Author(s):
Jeff Bezanson
Location
Ballroom (CDE)
Date
jul Wed 18
Days Raw Files
Start
11:55
First Raw Start
11:54
Duration
20:00
Offset
0:00:27
End
12:15
Last Raw End
12:18
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00:00
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22 min.
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Julia is a dynamic language designed for technical applications and high performance. Its design is based on a sophisticated but unobtrusive type system, type inference, multiple dispatch instead of class-based OO, and a code generator based on LLVM. These features work together to run high-level code efficiently even without type declarations. At the same time, the type system provides useful expressiveness for designing libraries, enables forms of metaprogramming not traditionally found in dynamic languages, and creates the possibility of statically compiling whole programs and libraries. This combination of high performance and expressiveness makes it possible for most of Julia's standard library to be written in Julia itself, with an interface to call existing C and Fortran libraries. We will discuss some ways that Python and Julia can interoperate, and compare Julia's current capabilities to Python and NumPy.
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2012-07-18/09_54_33.dv
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