SIMD Vectorization in Julia

SIMD Vectorization in Julia(software.intel.com)

126 points by mlubin 11 years ago | 11 comments

mlu 11 years ago |

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 11 years ago | |

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 11 years ago | |

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 11 years ago | |

Doesn't it have mmap?

ninjin 11 years ago | | |

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...

pjmlp 11 years ago |

Julia is really shaping up quite nicely.

etrain 11 years ago |

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.

hitlin37 11 years ago |

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

3JPLW 11 years ago | |

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

2. https://www.youtube.com/watch?v=zFxaK0ufL04

tokai 11 years ago | |

Really nice to see Intel giving Julia some attention.

z3t4 11 years ago |

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