Tim, The 11 seconds was entirely spent in compiling the 6 functions in the file that I was working on. I had no 'using' or 'include' commands in the file; I think the time was entirely compilation.
I would be happy with the mode you suggest. I see it as a development mode; ideally in addition to quick compilation, changed files are detected and automatically reloaded and in general the 'compile' part of the edit-compile-test cycle is very short and we can essentially iterate on an edit-test cycle. Then we can have a compilation mode, in which the LLVM can do all kinds of fancy tricks, and it's ok that it takes 11 seconds to compile a relatively small module. I'm glad to hear about Jameson's caching work; I think this is important. Chris On Sunday, November 23, 2014 12:21:43 PM UTC-8, Tim Holy wrote: > > I think Christian has a good point. > > The time in Julia does depend on how many extra packages, etc, one loads. > In > Jameson's up-and-coming caching work, packages should get compiled the > first > time you use them, and thereafter be very fast to load. But if your > package > has a large codebase on its own, this will only get you so far. > > Christian, how much of that 11 seconds was _parsing_ (statements like > `using` > or `include`) vs. the time to _compile_ (the delay when you run something > like > `runtest(A)`, where `A` is the input)? If it's mostly the latter, then I > wonder if it would be helped by having a mode in which compilation is less > specialized, and therefore fewer methods need to be built. > > --Tim > > On Sunday, November 23, 2014 08:06:34 PM Milan Bouchet-Valat wrote: > > Le dimanche 23 novembre 2014 à 10:32 -0800, Christian Peel a écrit : > > > Stefan, > > > > > > Thank you for the explanation of the issues with line numbers and > > > LLVM, and how it compares with Matlab and Julia. > > > > > > In your recent papers and on http://julialang.org/benchmarks/ we can > > > see that Julia is among the fastest languages to *run*. I'd be very > > > interested to see the speed with which a language can detect a syntax > > > error. In an informal test a minute ago using similar 500-line > > > functions in both languages, Matlab was able to detect my syntax error > > > in under a second. Julia took around 11 seconds. So Matlab has an > > > edit-test cycle; it's very quick. Julia has what feels very much > > > like an edit-compile-test cycle; the compile step is very visible. > > > Matlab allows me to make thousands of edits per day and quickly > > > develop high-level code; Julia development feels slower overall, even > > > with faster runtime. Tamas made a suggestion above which I > > > interpreted as asking for a flag that would allow one to have faster > > > syntax error checking at the expense of slower runtime. I agree with > > > you that it would be ideal to have 'always fast debug info'; if for > > > some reason you can't get the syntax error checking down; I would > > > support a flag such as Tamas suggests. > > > > Stefan was talking about getting backtraces when runtime errors happen. > > As regards simple syntax errors, I don't think anything implies Julia > > must be slow to report them: just like Matlab, she needs to parse the > > code before doing anything with it. The compilation step, which involves > > more work in Julia, happens later (and only if the code is correct). > > > > But most of the time your code won't probably include syntax errors: in > > that case, improving compilation times may prove trickier. > > > > > > Regards > > > > > Thanks for your work! > > > > > > Chris > > > > > > On Sunday, November 23, 2014 5:44:32 AM UTC-8, Stefan Karpinski wrote: > > > I'd rather shoot for always fast, always good debug info :-). > > > We're not very far off, we just need to keep upstreaming > > > improvements to LLVM. > > > > > > On Sun, Nov 23, 2014 at 8:16 AM, Tamas Papp <[email protected]> > > > > > > > wrote: > > > On Sun, Nov 23 2014, Stefan Karpinski > > > > > > <[email protected]> wrote: > > > > On Sun, Nov 23, 2014 at 6:02 AM, Christian Peel > > > > > > <[email protected]> wrote: > > > >> Milan, > > > >> > > > >> Thanks for the comments. I also am confident that > > > > > > info about what line > > > > > > >> errors occur on will improve. > > > > > > > > For some context, the reason traditional dynamic > > > > > > systems like e.g. Matlab > > > > > > > or Python, don't have this issue is that they use > > > > > > interpreters and > > > > > > > literally walk through a fairly faithful > > > > > > representation of the code you > > > > > > > wrote, including line number annotations. Thus, when > > > > > > an error occurs, it's > > > > > > > a trivial matter to report the line number you are > > > > > > currently interpreting – > > > > > > > the parsed code representation is the exact thing > > > > > > you're operating on when > > > > > > > the error occurs. Moreover, to give a full stack > > > > > > trace, you simply need to > > > > > > > walk back through the stack data structure that the > > > > > > interpreter maintains > > > > > > > as it executes the program. > > > > > > > > In a JIT system like Julia, things are quite > > > > > > different. By the time your > > > > > > > code is running, it no longer closely resembles the > > > > > > code you wrote and > > > > > > > there is no interpreter and the "stack" is the > > > > > > actual stack. All of this is > > > > > > > what gives you C-like performance. When an error > > > > > > occurs, the error message > > > > > > > and stack trace have to be determined from the > > > > > > machine code that is > > > > > > > currently executing and the stack trace is derived > > > > > > from *the* stack, not > > > > > > > some data structure. All this is further complicated > > > > > > by the fact that > > > > > > > LLVM's JIT does not tend to produce debug data quite > > > > > > as well as the static > > > > > > > compilation infrastructure does. People don't seem > > > > > > to use debuggers on > > > > > > > JITed code or particularly care about backtraces, so > > > > > > we're pushing the > > > > > > > envelope here, but things are getting better quite > > > > > > steadily. > > > > > > I am wondering if the approach used by Common Lisp > > > implementations could > > > be applicable in Julia. In Common Lisp, one can have > > > various > > > optimization and debug level settings, eg > > > > > > (optimize (speed 3) (safety 0) (debug 0)) > > > > > > will produce very fast code (comparable to C on modern > > > implementations > > > if type info is available), with the trade-off that > > > one has very little > > > debug information, while > > > > > > (optimize (debug 3)) > > > > > > does the opposite. Of course, the function needs to be > > > recompiled for > > > these to take effect, but given that you can always > > > modify the image in > > > a backtrace (AFAIK you can do the same in Julia), it > > > works fine in > > > practice. > > > > > > Best, > > > > > > Tamas > >
