4 ms·
Last week I compared a standard regression command across several statistical programs (R, Py, Stata, Matlab, Julia, etc) on a stupidly simple 10x5 matrix, and
by zzleeper 8y ago
Last week I compared a standard regression command across several statistical programs (R, Py, Stata, Matlab, Julia, etc) on a stupidly simple 10x5 matrix, and Julia took ages to run (this is on windows). Even loading the CSV file took seconds in Julia, hundreds of times slower than all of the other packages put together.
I'm not sure if they just suck at Windows, but I was extremely dissapointed... :/
- krull10 8y agoThe first time you run code in a REPL Julia will have to compile it. So starting a new REPL, and trying to solve a very small problem one time will currently be a terrible workflow for Julia (unless you don’t care that it may take a few seconds). If you had run your code again within the same REPL you’d see it runs substantially faster the second time. Likewise, if you had tried to process a large enough matrix or CSV that compilation time should be a minimal subset of the total computing time, and probably not noticeable. It isn’t a language limitation, just a tooling limitation. The startup times have been getting better as Julia develops, but there is still noticeable overhead the first time code gets run in a new REPL...
- jpfr 8y agoProbably it took ages for the first invocation. Because that is when the compiler has to produce native code. The compiled codes then is super fast. Just invoke it a second time (in the REPL) to see the difference.