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R-Bloggers has recently been buzzing about Julia, the new kid on the statistical programming block. Julia, however, is hardly the sole contender for the market of R defectors, with Clojure-fork Incanter generating buzz as well. Even with these two making noise, I think there’s a huge point that everyone is missing, and it’s front-and-center on the Julia homepage:
| Julia | Python | Matlab | Octave | R | JavaScript | |
|---|---|---|---|---|---|---|
| 3f670da0 | 2.7.1 | R2011a | 3.4 | 2.14.2 | V8 3.6.6.11 | |
| fib | 1.97 | 31.47 | 1336.37 | 2383.80 | 225.23 | 1.55 |
| parse_int | 1.44 | 16.50 | 815.19 | 6454.50 | 337.52 | 2.17 |
| quicksort | 1.49 | 55.84 | 132.71 | 3127.50 | 713.77 | 4.11 |
| mandel | 5.55 | 31.15 | 65.44 | 824.68 | 156.68 | 5.67 |
| pi_sum | 0.74 | 18.03 | 1.08 | 328.33 | 164.69 | 0.75 |
| rand_ mat_stat | 3.37 | 39.34 | 11.64 | 54.54 | 22.07 | 8.12 |
| rand_ mat_mul | 1.00 | 1.18 | 0.70 | 1.65 | 8.64 | 41.79 |
Julia kicks ass on the benchmarks, but it also has a severe uphill battle. It’s new, it’s Linux, it’s command-line-only, and it doesn’t have support for the wide array of Statistical functionality available in R. But besides the obvious my-language-can-beat-up-your-language comparisons, notice anything interesting? Think orders of magnitude?
Javascript performs nearly as well as Julia down the board. This nearly floored me.
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