My argument does be purely about performance. However, you won't know if your Julia code is 1.3x slower than C or 130x slower, until you write the C code.
On Thursday, September 24, 2015 at 7:14:46 PM UTC+2, Tom Breloff wrote: > > Unless you are experts of compilers and Julia language, you can never know >> whether your code give you an edge or not > > > Isn't this true of all languages? How do you know you did that C pointer > arithmetic correctly? Or that python didn't silently clobber your data? > This is why integration testing is important... to make sure that > everything works together as expected. Having an implementation in another > language only shows that the results match... they could still both be > wrong! > > If your argument is purely about performance (not correctness), then who > cares if julia is 1.3x slower than C... you wrote 100x the functionality in > the same development time, which left you time to optimize things you > normally wouldn't. > > On Thu, Sep 24, 2015 at 1:00 PM, Sisyphuss <[email protected] > <javascript:>> wrote: > >> What do you do when there's no code to compare? >>> >> This is a good point! When I write a piece of Julia code, how do I know I >> wrote it correctly? Should I write a C version to prove it? >> This is what I called the risk to write Julia code. Unless you are >> experts of compilers and Julia language, you can never know whether your >> code give you an edge or not. >> >> On Thursday, September 24, 2015 at 2:17:25 PM UTC+2, Marcio Sales wrote: >>> >>> Wow. All this discussion to make Julia only *as fast as* the old >>> scripting languages? I gotta say that worried me a bit. What do you do when >>> there's no code to compare? How will you know that it was really a good >>> idea switching from Matlab/Python to Julia? >>> >>> Considering what the develops proudly advertize about performance (what >>> I think is why most people would even consider changing to it), shouldn't >>> the language be designed as to put the user in the best performant >>> direction most of the time? Matlab does a good job on that with fewer but >>> simplified and efficient data structures, supporting vectorized code etc. >>> In my short experience with Julia, it seems that there are a lot of ways to >>> do the same thing, some of which very bad in terms of performance, like the >>> original code in this post. If Julia can't be easily faster and less >>> verbose than R for example, we could just forget about it... >>> >>> >>> >>> >>> >>> >>> >
