Julia Language Seeks To Be the C For Numerical Computing

concealment writes in with an interview with a creator of the (fairly) new language Julia designed for number crunching. Quoting Infoworld: "InfoWorld: When you say technical computing, to what type of applications are you specifically referring? Karpinski: It's a broad category, but it's pretty much anything that involves a lot of number-crunching. In my own background, I've done a lot of linear algebra but a fair amount of statistics as well. The tool of choice for linear algebra tends to be Matlab. The tool of choice for statistics tends to be R, and I've used both of those a great deal. But they're not really interchangeable. If you want to do statistics in Matlab, it's frustrating. If you want to do linear algebra in R, it's frustrating. InfoWorld: So you developed Julia with the intent to make it easier to build technical applications? Karpinski: Yes. The idea is that it should be extremely high productivity. To that end, it's a dynamic language, so it's relatively easy to program, and it's got a very simple programming model. But it has extremely high performance, which cuts out [the need for] a third language [C], which is often [used] to get performance in any of these other languages. I should also mention NumPy, which is a contender for these areas. For Matlab, R, and NumPy, for all of these options, you need to at some point drop down into C to get performance. One of our goals explicitly is to have sufficiently good performance in Julia that you'd never have to drop down into C." The language implementation is licensed under the GPL. Lambda the Ultimate has a bit of commentary on the language, and an R programmer gives his two cents on the language.

Comments

Sage

[donaggie03]

I use Sage quite a bit. It's basically a wrapper for almost all the mathematics software available. http://www.sagemath.org/ While you still need to drop down to C for great performance, it solves a lot of the interoperability issues discussed. In other words, take the example from the summary: from Sage, you can call Matlab commands and then immediately use the results with R commands. Sage works through a web browser, and it's based on Python, which is a plus.

FORTRAN?

[TWX]

From wikipedia: "FORTRAN is a general-purpose, procedural, imperative programming language that is especially suited to numeric computation and scientific computing." Sounds to me like unless there's a particular weakness in FORTRAN that doesn't lend itself to workarounds or repair in newer versions of the language, there's already a numeric computation and scientific programming language that's well documented, mature, and widely distributed.

Re:FORTRAN?

[Anonymous Coward]

Second that. Modern FORTRAN kicks some serious butt and has a huge user and support base. Language snobs dismiss it as antiquated but they're usually referring to versions of the language that haven't been used since the 1980's. There are good reasons that current HPC developers use mostly FORTRAN and C, like good support for parallalization, global memory functions for clustering, and efficient compilers.

It's great that people make new tools and share them with others, but many times that effort could be put into making existing good tools even better.

Numerical Python

[Dr. Tom]

Robust, mature, fast, easy to use, side-by-side with .m it wins hands down, really no comparison, use Python.
Cython if you need to make it faster for the %5 of code that is too slow.

import numpy
import pylab

Re:The "C" for some field?

[swan5566]

The problem is that a lot of researchers and scientists who write these things aren't trained in good programming practices, and most of the good programmers don't have the background to do a lot of the advanced math stuff properly.

Re:The "C" for some field?

[jedwidz]

Then again, most of it is written by biologists.

You mean 'evolved by biologists'. They aren't strong believers in intelligent design.