Harry, I'm curious about 2 of your 3 last points: On Thursday, April 30, 2015 05:50:15 PM Harry B wrote: > (exceptions?, debugging, profiling tools)
We have exceptions. What aspect are you referring to? Debugger: yes, that's missing, and it's a huge gap. Profiling tools: in my view we're doing OK (better than Matlab, in my opinion), but what do you see as missing? --Tim > > Thanks > -- > Harry > > On Thursday, April 30, 2015 at 3:43:36 PM UTC-7, Páll Haraldsson wrote: > > 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....@gmail.com <javascript:>>: > >> 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.
