It seemed to me tuples where slow because of Any used. I understand tuples have been fixed, I'm not sure how.
I do not remember the post/all the details. Yes, tuples where slow/er than Python. Maybe it was Dict, isn't that kind of a tuple? Now we have Pair in 0.4. I do not have 0.4, maybe I should bite the bullet and install.. I'm not doing anything production related and trying things out and using 0.3[.5] to avoid stability problems.. Then I can't judge the speed.. Another potential issue I saw with tuples (maybe that is not a problem in general, and I do not know that languages do this) is that they can take a lot of memory (to copy around). I was thinking, maybe they should do similar to databases, only use a fixed amount of memory (a "page") with a pointer to overflow data.. 2015-04-30 22:13 GMT+00:00 Ali Rezaee <arv.ka...@gmail.com>: > 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. >> >> -- Palli.
