The Julia Programming Language

https://medium.com/@jshysing/the-julia-programming-language-for-number-crunching-87faad52ce08#.gf4qd530b

by bauta-steen • 10 years ago

66 35 10 years ago

jpfr2• 10 years ago

Julia and Matlab use the same (Fortran-based) linear algebra codes in the background.

I think it is great to see that a port of the Fortran code in Julia is nearly as fast as the Fortran code itself. So you can take Julia and write concise Matlab-style code (vectorized everywhere) or write imperative code (loops everywhere) and be equally fast.

Note that the graphs in the post are biased. The Fortran version uses a different algorithm for the solver.

Edit: The Matlab code is highly obfuscated. That is not good for the sake of comparison.

https://github.com/precisesimulation/julia-matlab-fortran-fe...

SixSigma• 10 years ago

Except Julia vectorised is not as fast as Julia looped.

so this :

    for i = 1:size(a)[1]
        a[i] = 2a[i] + 3b[i]
    end

is faster than

    a = 2a .+ 3b

(although I haven't benchmarked it)

Here's someone that did : http://aflyax.github.io/de_vectorization-runtime/

but in 0.5 there are speedups, apparently.

Here's a stack overflow discussion on it

http://stackoverflow.com/questions/27928502/julia-vectorized...

bauta-steen• 10 years ago

Very interesting, just tested on 0.5 and vectorized can still be a magnitude slower.

ulia> @time vect(x, y, a, b) 0.000038 seconds (29 allocations: 37.406 KB) 1×2 Array{Float64,2}:

julia> @time devect(x, y, a, b) 0.000006 seconds (10 allocations: 512 bytes) 1×2 Array{Float64,2}:

julia> @time vect(x, y, a, b) 0.002324 seconds (73 allocations: 3.075 MB) 416×2 Array{Float64,2}:

julia> @time devect(x, y, a, b) 0.000206 seconds (18 allocations: 23.359 KB) 416×2 Array{Float64,2}:

marmaduke4• 10 years ago

Matlab uses mkl for linear algebra, not the old school Fortran reference routines.

jpfr2• 10 years ago

Optionally. OpenBLAS and LAPACK are the default.

See here: https://github.com/JuliaLang/julia

  OpenBLAS — fast, open, and maintained basic linear algebra subprograms (BLAS) library, based on Kazushige Goto's famous GotoBLAS.

  LAPACK (>= 3.5) — library of linear algebra routines for solving systems of simultaneous linear equations, least-squares solutions of linear systems of equations, eigenvalue problems, and singular value problems.

  MKL (optional) – OpenBLAS and LAPACK may be replaced by Intel's MKL library

akssri• 10 years ago

It's probably (n^3 * n^2) vs (n^2 * n^2) , and not cubic vs linear as was stated, where (n = 1/h).

gnaddel• 10 years ago

I think Julias adoption is hindered by the lack of a real Julia-IDE aimed at data analysis. R has R-Studio, Python has Spyder, both of which are excellent nowadays. Julia has Juno in principle, but setup has never worked for me on multiple machines. The Julia language has a lot to offer, but there is no convenient way for people to give it a try that is comparable to what they have grown to expect from competing languages.

usernam2• 10 years ago

There's actually Jupyter. Then there's a julia backend for ess as well.

I'm using R regularly, and I couldn't care less for R-Studio. In our stat group, only 1 statistician out of 7 is using R-studio, while all of them are using R.

The IDE has very little to do with adoption.

cheriot• 10 years ago

There can be a difference between IDE choice among professionals and the role of an IDE in introducing people to the ecosystem. On ramps don't start at the target elevation.

ssivark• 10 years ago

Try Juno (a Julia IDE on top of Atom): http://junolab.org/

Recent JuliaCon talk allowing off the debugging features. https://m.youtube.com/watch?v=yDwUL3aRSRc

syntaxing3• 10 years ago

Does anyone here know any good debugger for Julia?

That being said, Julia is really fun language to learn if you know Python and Matlab already. Although in the beginning, I kept on forgetting whether it is following Matlab-like syntax or Python-like syntax for certain features.

One of the biggest problems that I had with Julia (which is one of the main reason I stopped using it) was the flexibility of the . notation. I use this feature in Python and Matlab a lot and I had some serious trouble trying to make it work or finding examples on how to use it. Though I think this problem will be fixed as the community grows.

ced5• 10 years ago

https://github.com/Keno/Gallium.jl for the debugger. It was still a bit unstable last time I tried it.

What are you referring to? Properties?

syntaxing3• 10 years ago

I mean the composite types that Julia offers. I was originally trying to use it to replicate some of the structure array that Matlab offers. Understandably this isn't the most efficient way to create a list, but it makes it so easy and clean to pass variables between functions when I'm trying to crunch some numbers quick. I tried getting used to calling sub-functions with a . (like Python) as well but didn't get too far.

drjesusphd• 10 years ago

I find it almost purely Matlab-like in syntax. What do you have in mind that's Python-like?

hasch• 10 years ago

Sorry for being old and still living in the Python world: I would like to see an additional benchmark for this based on Python+Numba.

DanielleMolloy• 10 years ago

Found this, and Python numpy compiled with MKL or using numba does quite well: https://www.scivision.co/speed-of-matlab-vs-python-numpy-num...

KKKKkkkk1• 10 years ago

I do not understand the notion of a fast language. I can buy the idea that a compiled (or JITted) language will run loops faster than an interpreted one. But writing a DGEMM in Julia or in Fortran is not what will make it fast, it's careful performance profiling and machine-specific optimizations that will do this. Apparently every language community has to learn this the hard way once it gets mature enough. See the following for an example from the C++ world: https://youtu.be/hfn0BVOegac?t=30m40s.

dagss2• 10 years ago

The explanation is simply that people are happy to get a 10x speedup even if 100x is possible in theory.

Python + NumPy has a large mindshare, and can be 10x from Fortran/Julia even if you use NumPy vectorization. And often loops are simpler or required anyway (dropping Python to 1000x slower).

Languages like Julia and Fortran also allows you to hit peak where you are limited by memory bandwidth, which is most of the time (not for DGEMM though...)

gpderetta• 10 years ago

Some languages are explicitly designed to be easier to optimize than others. Both for their compiler and, most importantly, their users.

The mythical Sufficiently Smart Compiler, is, well, a myth.

marmaduke4• 10 years ago

Matlab delegates a lot of heavy lifting to native routines like MKL and otherwise has a JIT for user code. For workloads which fit on a single workstation, it's usually a reasonable performer, for a lot less effort than the alternatives.

That said it costs too much and doesn't scale well to multimachine workloads. The language is also quite quirky by modern standards.

dagss2• 10 years ago

Are you saying that Matlab is "a lot less effort" than Julia or NumPy? (Assuming cases where these alternatives have the functionality in toolbox of course)

marmaduke4• 10 years ago

yes (?)

hellofunk• 10 years ago

I've been curious for a long time why, despite the claims that Julia is so fast for scientific computing, no one has actually provided any Julia implementations for all the scientific and number-crunching problems over at the popular benchmarks game [0]. It would seem to me a great place to advertise the language's speed, so no news is curious news there.

[0] http://benchmarksgame.alioth.debian.org/

simonbyrne• 10 years ago

See https://github.com/JuliaLang/julia/tree/master/test/perf/sho...

igouy• 10 years ago

http://benchmarksgame.alioth.debian.org/play.html#languagex

scottlocklin• 10 years ago

This benchmark is rubbish, and the author doesn't know what he is talking about. "Knowing a little about Matlab should really be surprised to see that it performed just as fast as Julia and Fortran. This is due to the heavily vectorized and optimized Matlab assembly routine. "

Complete gibberish. Matlab (like Octave and the parts of Julia tested here) is made of Fortran parts. Were this an actual benchmark it would have spectacularly outperformed Matlab, Octave and Julia.

syphilis22• 10 years ago

Having been disappointed with the performance of Octave and Python as Matlab alternatives, Julia looks very compelling. Is anyone aware of any thorns to watch out for?

ced5• 10 years ago

Do you have specific concerns? What kind of project would you like to do? Julia is nearly as convenient as Python, and can be nearly as fast as Fortran, but it has neither the polish or large community/library of those languages.

syphilis22• 10 years ago

I'm usually concerned with how easy it is to manipulate text: sort, search, delimit, and replace substrings. Especially given an array or matrix of string data.

Matlab is in part a wonderful IDE that lets me run any segment of code and maintain variables. This is perhaps outside of the language, but I did see Julia's blog post about Juno and it looks useful.

Maybe the biggest concern is documentation for functions. Is it quick to look up any function? Is it quick to discover available functions relevant to a task?

The Julia Programming Language

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