[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.

[simonster]

In Julia 0.3 there are SharedArrays on Linux/OS X: http://julia.readthedocs.org/en/latest/stdlib/base/#shared-a...

Hopefully there will also be multithreading someday, although right now it's still a work in progress: https://github.com/JuliaLang/julia/tree/threads

[ihnorton]

Shared-memory multiprocessing is supported in the latest release on linux and mac. Multi-threading is in development and has seen some significant progress over the past few months.

> But I'm sure, it's on the right track.

Yes, and it is worth pointing out that both this article and the @simd feature were written by Arch Robison - who is also the architect of Intel's Threading Building Blocks.

[illumen]

Doesn't it have mmap?

[ninjin]

It does, in the base library in fact:

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...