I just read through all of that very interesting thread on exceptions... it seems that Stefan was trying to reinvent the wheel, without knowing it.
Everybody interested in exception handling should go look up CLU... Julia seems to have gotten a lot of ideas from CLU (possibly rather indirectly, through C++, Java, Lua...). CLU had this well handled 40 years ago ;-) Scott On Friday, May 1, 2015 at 12:42:47 AM UTC-4, Harry B wrote: > > Sorry my comment wasn't well thought out and a bit off topic. On > exceptions/errors my issue is this > https://github.com/JuliaLang/julia/issues/7026 > On profiling, I was comparing to Go, but again off topic and I take my > comment back. I don't have any intelligent remarks to add (yet!) :) > Thank you for the all the work you are doing. > > On Thursday, April 30, 2015 at 7:00:01 PM UTC-7, Tim Holy wrote: >> >> 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. >> >>
