Strings have long been a performance sore-spot in julia, so we're glad Scott is hammering on that topic.
For "interpreted" code (including Julia with Any types), it's very possible that Python is and will remain faster. For one thing, Python is single- dispatch, which means that when the interpreter has to go look up the function corresponding to your next expression, typically the list of applicable methods is quite short. In contrast, julia sometimes has to sort through huge method tables to determine the appropriate one to dispatch to. Multiple dispatch adds a lot of power to the language, and there's no performance cost for code that has been compiled, but it does make interpreted code slower. Best, --Tim On Thursday, April 30, 2015 10:34:20 PM Páll Haraldsson wrote: > Interesting.. does that mean Unicode then that is esp. faster or something > else? > > >800x faster is way worse than I thought and no good reason for it.. > > I'm really intrigued what is this slow, can't be the simple things like say > just string concatenation?! > > You can get similar speed using PyCall.jl :) > > For some obscure function like Levenshtein distance I might expect this (or > not implemented yet in Julia) as Python would use tuned C code or in any > function where you need to do non-trivial work per function-call. > > > I failed to add regex to the list as an example as I was pretty sure it was > as fast (or faster, because of macros) as Perl as it is using the same > library. > > Similarly for all Unicode/UTF-8 stuff I was not expecting slowness. I know > the work on that in Python2/3 and expected Julia could/did similar. > > 2015-04-30 22:10 GMT+00:00 Scott Jones <scott.paul.jo...@gmail.com>: > > Yes... Python will win on string processing... esp. with Python 3... I > > quickly ran into things that were > 800x faster in Python... > > (I hope to help change that though!) > > > > Scott > > > > On Thursday, April 30, 2015 at 6:01:45 PM UTC-4, Páll Haraldsson wrote: > >> I wouldn't expect a difference in Julia for code like that (didn't > >> check). But I guess what we are often seeing is someone comparing a tuned > >> Python code to newbie Julia code. I still want it faster than that code.. > >> (assuming same algorithm, note row vs. column major caveat). > >> > >> The main point of mine, *should* Python at any time win? > >> > >> 2015-04-30 21:36 GMT+00:00 Sisyphuss <zhengw...@gmail.com>: > >>> This post interests me. I'll write something here to follow this post. > >>> > >>> The performance gap between normal code in Python and badly-written code > >>> in Julia is something I'd like to know too. > >>> As far as I know, Python interpret does some mysterious optimizations. > >>> For example `(x**2)**2` is 100x faster than `x**4`. > >>> > >>> 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? > >>>> Received server disconnect: b0 'Idle Timeout' > >>>> > >>>> 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.
