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Haha, completely true. Still it's not as explicit as it could be. Am I being nitpicky? Sure, but the word "graphical" only appears in the context of the IJulia

Haha, completely true. Still it's not as explicit as it could be. Am I being nitpicky? Sure, but the word "graphical" only appears in the context of the IJulia notebook. Again if you want to really sell this to scientist, really explain the graphical stuff since plotting is an essential part of doing computational mathematics. It is interesting your example about Matlab. Did you design it with vectorisation in mind? Not to dismiss your coding skills, but I've seen a lot of poorly-designed Matlab code that just doesn't exploit the Matlab language. The office mate I mentioned above was one of them. I took a look at his code once and vectorised it and something that was taking 10 minutes took 30 seconds. I am definitely going to use it on future projects. Since right now I'm finishing up stuff it wouldn't make sense to re-write all my Matlab processing routines into Julia. I think a good specifically designed open-source version of Matlab is needed. Yes I know about Octave but I've never seen anyone really use Octave since it never seems to work for more complicated things.

rrock 13y ago
> It is interesting your example about Matlab. Did you design it with vectorisation in mind? Sure did. I've been using MATLAB since before it had a JIT. In fact, for the Julia code I didn't even devectorize which would play to its relative strengths. I also didn't have to pull out a profiler for all of this, it just happened with the first stab at it. >Since right now I'm finishing up stuff it wouldn't make sense to re-write all my Matlab processing routines into Julia. No need to, just do what I did and pick one function that's called in a tight loop and try testing that. If you don't find the performance that you'd like, post a code example on the julia-users mailing list (under the Community link on the website). Folks will either help you to 1) improve your code with Julia's design in mind, or 2) use your code as a test case to improve Julia itself. The community is very helpful. A peek at the performance tips section in the manual is a good idea. Don't forget to wrap your code in a function for testing, and to "warm up" your code (= give the JIT a chance) before calling the @time macro on your function. Good luck!

rrock 13y ago

> It is interesting your example about Matlab. Did you design it with vectorisation in mind? Sure did. I've been using MATLAB since before it had a JIT. In fact, for the Julia code I didn't even devectorize which would play to its relative strengths. I also didn't have to pull out a profiler for all of this, it just happened with the first stab at it. >Since right now I'm finishing up stuff it wouldn't make sense to re-write all my Matlab processing routines into Julia. No need to, just do what I did and pick one function that's called in a tight loop and try testing that. If you don't find the performance that you'd like, post a code example on the julia-users mailing list (under the Community link on the website). Folks will either help you to 1) improve your code with Julia's design in mind, or 2) use your code as a test case to improve Julia itself. The community is very helpful. A peek at the performance tips section in the manual is a good idea. Don't forget to wrap your code in a function for testing, and to "warm up" your code (= give the JIT a chance) before calling the @time macro on your function. Good luck!