Why I’m Betting on Julia
By Evan Miller
January 23, 2014
Translations: Japanese
The problem with most programming languages is they’re designed by language geeks, who tend to worry about things that I don’t much care for. Safety, type systems, homoiconicity, and so forth. I’m sure these things are great, but when I’m messing around with a new project for fun, my two concerns are 1) making it work and 2) making it fast. For me, code is like a car. It’s a means to an end. The “expressiveness” of a piece of code is about as important to me as the “expressiveness” of a catalytic converter.
This approach to programming is often (derisively) called cowboy coding. I don’t think a cowboy is quite the right image, because a cowboy must take frequent breaks due to the physical limitations of his horse. A better aspirational image is an obsessed scientist who spends weeks in the laboratory and emerges, bleary-eyed, exhausted, and wan, with an ingenious new contraption that possibly causes a fire on first use.
Enough about me. Normally I use one language to make something work, and a second language to make it fast, and a third language to make it scream. This pattern is fairly common. For many programmers, the prototyping language is often Python, Ruby, or R. Once the code works, you rewrite the slow parts in C or C++. If you are truly insane, you then rewrite the inner C loops using assembler, CUDA, or OpenCL.
Unfortunately, there’s a big wall in between the prototyping language and C, and another big wall between C and assembler. Besides having to learn three different languages to get the job done, you have to mentally switch between the layers of abstraction. At a more quotidian level, you have to write a significant amount of glue code, and often find yourself switching between different source files, different code editors, and disparate debuggers.
I read about Julia a while back, and thought it sounded cool, but not like something I urgently needed. Julia is a dynamic language with great performance. That’s nice, I thought, but I’ve already invested a lot of time putting a Ferrari engine into my VW Beetle — why would I buy a new car? Besides, nowadays a number of platforms — Java HotSpot, PyPy, and asm.js, to name a few — claim to offer “C performance” from a language other than C.
Only later did I realize what makes Julia different from all the others. Julia breaks down the second wall — the wall between your high-level code and native assembly. Not only can you write code with the performance of C in Julia, you can take a peek behind the curtain of any function into its LLVM Intermediate Representation as well as its generated assembly code — all within the REPL. Check it out.
emiller ~/Code/julia (master) ./julia
_
_ _ _(_)_ | A fresh approach to technical computing
(_) | (_) (_) | Documentation: http://docs.julialang.org
_ _ _| |_ __ _ | Type "help()" to list help topics
| | | | | | |/ _` | |
| | |_| | | | (_| | | Version 0.3.0-prerelease+261 (2013-11-30 12:55 UTC)
_/ |\__'_|_|_|\__'_| | Commit 97b5983 (0 days old master)
|__/ | x86_64-apple-darwin12.5.0
julia> f(x) = x * x
f (generic function with 1 method)
julia> f(2.0)
4.0
julia> code_llvm(f, (Float64,))
define double @julia_f662(double) {
top:
%1 = fmul double %0, %0, !dbg !3553
ret double %1, !dbg !3553
}
julia> code_native(f, (Float64,))
.section __TEXT,__text,regular,pure_instructions
Filename: none
Source line: 1
push RBP
mov RBP, RSP
Source line: 1
vmulsd XMM0, XMM0, XMM0
pop RBP
ret
Bam — you can go from writing a one-line function to inspecting its LLVM-optimized X86 assembler code in about 20 seconds.
So forget the stuff you may have read about Julia’s type system, multiple dispatch and homoiconi-whatever. That stuff is cool (I guess), but if you’re like me, the real benefit is being able to go from the first prototype all the way to balls-to-the-wall multi-core SIMD performance optimizations without ever leaving the Julia environment.
That, in a nutshell, is why I’m betting on Julia. I hesitate to make the comparison, but it’s poised to do for technical computing what Node.js is doing for web development — getting disparate groups of programmers to code in the same language. With Node.js, it was the front-end designers and the back-end developers. With Julia, it’s the domain experts and the speed freaks. That is a major accomplishment.
Julia’s only drawback at this point is the relative dearth of libraries — but the language makes it unusually easy to interface with existing C libraries. Unlike with native interfaces in other languages, you can call C code without writing a single line of C, and so I anticipate that Julia’s libraries will catch up quickly. From personal experience, I was able to access 5K lines of C code using about 150 lines of Julia — and no extra glue code in C.
If you work in a technical group that’s in charge of a dizzying mix of Python, C, C++, Fortran, and R code — or if you’re just a performance-obsessed gunslinging cowboy shoot-from-the-hip Lone Ranger like me — I encourage you to download Julia and take it for a spin. If you’re hesitant to complicate your professional life with Yet Another Programming Language, think of Julia as a tool that will eventually help you reduce the number of languages that your project depends on.
I almost neglected to mention: Julia is actually quite a nice language, even ignoring its excellent performance characteristics. I’m no language aesthete, but learning it entailed remarkably few head-scratching moments. At present Julia is in my top 3 favorite programming languages.
Finally, you’ll find an active and supportive Julia community. My favorite part about the community is that it is full of math-and-science types who tend to be very smart and very friendly. That’s because Julia was not designed by language geeks — it came from math, science, and engineering MIT students who wanted a fast, practical language to replace C and Fortran. So it’s not designed to be beautiful (though it is); it’s designed to give you answers quickly. That, for me, is what computing is all about.
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