

SIMD Vectorization in Julia - mlubin
https://software.intel.com/en-us/articles/vectorization-in-julia

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mlu
This looks great! I used Julia in my Master's thesis and it was very fast and
easy to use. One thing however was annoying: The lack of shared memory
parallel computing. Unfortunately, in Julia, each parallel process has its own
memory such that you have to keep multiple instances of your data and/or move
data around all the time. This can be a deal breaker if working with very
large data sets and actually is preventing me from using Julia even more. But
I'm sure, it's on the right track.

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illumen
Doesn't it have mmap?

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ninjin
It does, in the base library in fact:

[https://github.com/JuliaLang/julia/blob/master/base/mmap.jl](https://github.com/JuliaLang/julia/blob/master/base/mmap.jl)

Here is the relevant documentation:

[http://julia.readthedocs.org/en/latest/stdlib/base/#memory-m...](http://julia.readthedocs.org/en/latest/stdlib/base/#memory-
mapped-i-o)

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pjmlp
Julia is really shaping up quite nicely.

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etrain
Looks like a very nice feature - but would be great if they provided some
actual benchmarks showing when vectorization is faster than not. For example,
if you're bound by memory bandwidth, no amount of extra compute is going to
help you.

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hitlin37
as always, intel comes up with nice tutorial from time to time.

~~~
3JPLW
The author of this piece, Arch Robison, has been an active contributor to
Julia. Not only did he write this tutorial, but he actually contributed the
`@simd` implementation himself.[1]

He also presented this information as a talk at Juliacon back in June.[2]

1\.
[https://github.com/JuliaLang/julia/pull/5355](https://github.com/JuliaLang/julia/pull/5355)

2\.
[https://www.youtube.com/watch?v=zFxaK0ufL04](https://www.youtube.com/watch?v=zFxaK0ufL04)

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z3t4
This needs to be ported to JavaScript, or at least update the Math library to
include Vectors.

