They were interesting questions. I would also like to know why poorly written Julia code sometimes performs worse than similar python code, especially when tuples are involved. Did you say it was fixed?
On Thursday, April 30, 2015 at 9:58:35 PM UTC+2, Páll Haraldsson wrote: > > Hi, > > [As a best language is subjective, I'll put that aside for a moment.] > > Part I. > > The goal, as I understand, for Julia is at least within a factor of two of > C and already matching it mostly and long term beating that (and C++). > [What other goals are there? How about 0.4 now or even 1.0..?] > > While that is the goal as a language, you can write slow code in any > language and Julia makes that easier. :) [If I recall, Bezanson mentioned > it (the global "problem") as a feature, any change there?] > > > I've been following this forum for months and newbies hit the same issues. > But almost always without fail, Julia can be speed up (easily as Tim Holy > says). I'm thinking about the exceptions to that - are there any left? And > about the "first code slowness" (see Part II). > > Just recently the last two flaws of Julia that I could see where fixed: > Decimal floating point is in (I'll look into the 100x slowness, that is > probably to be expected of any language, still I think may be a > misunderstanding and/or I can do much better). And I understand the tuple > slowness has been fixed (that was really the only "core language" defect). > The former wasn't a performance problem (mostly a non existence problem and > correctness one (where needed)..). > > > Still we see threads like this one recent one: > > https://groups.google.com/forum/#!topic/julia-users/-bx9xIfsHHw > "It seems changing the order of nested loops also helps" > > Obviously Julia can't beat assembly but really C/Fortran is already close > enough (within a small factor). The above row vs. column major (caching > effects in general) can kill performance in all languages. Putting that > newbie mistake aside, is there any reason Julia can be within a small > factor of assembly (or C) in all cases already? > > > Part II. > > Except for caching issues, I still want the most newbie code or > intentionally brain-damaged code to run faster than at least > Python/scripting/interpreted languages. > > Potential problems (that I think are solved or at least not problems in > theory): > > 1. I know Any kills performance. Still, isn't that the default in Python > (and Ruby, Perl?)? Is there a good reason Julia can't be faster than at > least all the so-called scripting languages in all cases (excluding small > startup overhead, see below)? > > 2. The global issue, not sure if that slows other languages down, say > Python. Even if it doesn't, should Julia be slower than Python because of > global? > > 3. Garbage collection. I do not see that as a problem, incorrect? Mostly > performance variability ("[3D] games" - subject for another post, as I'm > not sure that is even a problem in theory..). Should reference counting > (Python) be faster? On the contrary, I think RC and even manual memory > management could be slower. > > 4. Concurrency, see nr. 3. GC may or may not have an issue with it. It can > be a problem, what about in Julia? There are concurrent GC algorithms > and/or real-time (just not in Julia). Other than GC is there any big > (potential) problem for concurrent/parallel? I know about the threads work > and new GC in 0.4. > > 5. Subarrays ("array slicing"?). Not really what I consider a problem, > compared to say C (and Python?). I know 0.4 did optimize it, but what > languages do similar stuff? Functional ones? > > 6. In theory, pure functional languages "should" be faster. Are they in > practice in many or any case? Julia has non-mutable state if needed but > maybe not as powerful? This seems a double-edged sword. I think Julia > designers intentionally chose mutable state to conserve memory. Pros and > cons? Mostly Pros for Julia? > > 7. Startup time. Python is faster and for say web use, or compared to PHP > could be an issue, but would be solved by not doing CGI-style web. How > good/fast is Julia/the libraries right now for say web use? At least for > long running programs (intended target of Julia) startup time is not an > issue. > > 8. MPI, do not know enough about it and parallel in general, seems you are > doing a good job. I at least think there is no inherent limitation. At > least Python is not in any way better for parallel/concurrent? > > 9. Autoparallel. Julia doesn't try to be, but could (be an addon?). Is > anyone doing really good and could outperform manual Julia? > > 10. Any other I'm missing? > > > Wouldn't any of the above or any you can think of be considered > performance bugs? I know for libraries you are very aggressive. I'm > thinking about Julia as a core language mostly, but maybe you are already > fastest already for most math stuff (if implemented at all)? > > > I know to get the best speed, 0.4 is needed. Still, (for the above) what > are the problems for 0.3? Have most of the fixed speed issues been > backported? Is Compat.jl needed (or have anything to do with speed?) I > think slicing and threads stuff (and global?) may be the only exceptions. > > Rust and some other languages also claim "no abstraction penalty" and > maybe also other desirable things (not for speed) that Julia doesn't have. > Good reason it/they might be faster or a good reason to prefer for > non-safety related? Still any good reason to choose Haskell or Erlang? I do > not know to much about Nim language that seems interesting but not clearly > better/faster. Possibly Rust (or Nim?) would be better if you really need > to avoid GC or for safety-critical. Would there be a best complementary > language to Julia? > > > Part III. > > Faster for developer time not CPU time. Seems to be.. (after a short > learning curve). This one is subjective, but any languages clearly better? > Right metric shouldn't really be to first code that seems right but > bug-free or proven code. I'll leave that aside and safe-critical issues. > > -- > Palli. > >
