Linear Regression 3 Ways in Julia

by luu • 13 years ago

81 20 13 years ago

therobot24• 13 years ago

every time i see a Julia post i want to convert my Matlab code and start anew - then i remember seeing a blog some time ago where the vectorizations i performed to speed up my Matlab code would be faster as for-loops in Julia. Is this still the case? If not, i'll probably consider the task much more seriously

asg• 13 years ago

That is still the case.

The following blog post goes into the reasons, and the remedies. In particular, there are ways to automatically devectorise your code.

http://julialang.org/blog/2013/09/fast-numeric/

JulianMorrison• 13 years ago

I'm a little surprised they haven't done loop fusion optimizations like Haskell's "vector". Do they plan to?

blt• 13 years ago

devectorizing is a terrible step back in expressiveness. It shouldn't be hard to make a compiler that can compute the example without a bunch of temporary arrays. I would be shocked if heavy duty packages like Matlab make all those temporary arrays.

On the other hand, I've seen (and written) plenty of code that went through contortions to use vector operations in IDL (http://en.wikipedia.org/wiki/IDL_(programming_language)). It would be great to have a language where for loops and vector expressions are fast.

StefanKarpinski4• 13 years ago

There are certain things that are still slower when you write vectorized code than they would be if you manually devectorized them. The only reason that's not true in Matlab or Python is that loops are so slow.

The real point of Dahua's "Fast Numeric" blog post [1] is that you can devectorize in the high-level language if you need to, not that you always should. Most code is not a performance bottleneck and can use the most convenient form. In cases where you identify a real hotspot [2], in Matlab you'd have to resort to writing C code via MEX. In Julia, you do one or more of the following: (a) change some copying function to a mutating version by sticking a "!" on the end of it; (b) move an array allocation out of a loop and pass the pre-allocated array into a mutating function inside the loop; (c) manually devectorize some code if absolutely necessary – or use the Devectorize package to do it automatically. At no point do you ever have to resort to writing code in any other language.

Also keep in mind that slowness is not the only problem one encounters with numerical code – I've personally have much more frequent trouble with memory usage. I've written far too much insanely convoluted Matlab code to try to avoid blowing out the memory on a 100GB machine (or 1TB these days). It's generally just barely doable using find, sparse, sub2ind and ind2sub. If I had to resort to writing C code every time I encountered one of these memory problems, I would just write the damned thing in C and scrap Matlab altogether. On the other hand in Julia for such situations, I can just write a few for loops and not only do I avoid blowing out my memory, but the code gets faster too, and I'm never required to leave my high-level environment.

[1] https://news.ycombinator.com/item?id=6329237

[2] http://docs.julialang.org/en/latest/stdlib/profile/

minimax• 13 years ago

Does Julia do any of the copy-on-write tricks that other numerical packages do? I.e. when I make a copy of an array does it immediately need space for a full copy of the original?

StefanKarpinski4• 13 years ago

The julia-users mailing list discussion of this blog post:

https://groups.google.com/forum/#!topic/julia-users/T5YYKQzl...

tomrod2• 13 years ago

I'm impressed that these characters can be used in code:

Σ₁,Σ₀, σ²

Derbasti2• 13 years ago

Many moder programming languages allow arbitrary unicode symbols as variable names. I am now thinking about doing this in Python.

tomrod2• 13 years ago

Fair enough. I use Sublime and write in python on Ubuntu. How might I go about incorporating Unicode?

Gravityloss• 13 years ago

Might be nice to read, but I wouldn't want to edit code written like that, since the symbols could well be harder and slower to write on a normal keyboard / editor.

Linear Regression 3 Ways in Julia

Related Stories