Julia 0.3.12, that's a stone-age version of Julia. You should move to 0.5!
On Sat, 2016-11-19 at 16:42, Harish Kumar <[email protected]> wrote: > I am using Version 0.3.12 calling from python (pyjulia). I do LME fit with > 2.8 M rows and 60-70 Variables. It is taking 2 hours just to model (+ data > transfer time). Any tips? > using MixedModels > modelREML = lmm({formula}, dataset) > reml!(modelREML,true) > lmeModel = fit(modelREML) > fixedDF = DataFrame(fixedEffVar = coeftable(lmeModel).rownms,estimate > = coeftable(lmeModel).mat[:,1], > stdError = coeftable(lmeModel).mat[:,2],zVal = > coeftable(lmeModel).mat[:,3]) > > On Tuesday, February 23, 2016 at 9:16:47 AM UTC-6, Stefan Karpinski wrote: >> >> I'm glad that particular slow case got faster! If you want to submit some >> reduced version of it as a performance test, we could still include it in >> our perf suite. And of course, if you find that anything else has ever >> slowed down, please don't hesitate to file an issue. >> >> On Tue, Feb 23, 2016 at 9:55 AM, Jonathan Goldfarb <[email protected] >> <javascript:>> wrote: >> >>> Yes, understood about difficulty keeping track of regressions. I was >>> originally going to send a message relating up to 2x longer test time on >>> the same code on Travis, but it appears as though something has changed in >>> the nightly build available to CI that now gives significantly faster >>> builds, even though the previous poor performance had been dependable... >>> Evidently that build is not as up-to-date as I thought. Our code is >>> currently not open source, but should be soon after which I can share an >>> example. >>> >>> Thanks for your comments, and thanks again for your work on Julia. >>> >>> -Max >>> >>> >>> On Monday, February 22, 2016 at 11:12:58 AM UTC-5, Stefan Karpinski wrote: >>>> >>>> Yes, ideally code should not get slower with new releases – >>>> unfortunately, keeping track of performance regressions can be a bit of a >>>> game of whack-a-mole. Having examples of code whose speed has regressed is >>>> very helpful. Thanks to Jarrett Revels excellent work, we now have some >>>> great performance regression tracking infrastructure, but of course we >>>> always need more things to test! >>>> >>>> On Mon, Feb 22, 2016 at 9:58 AM, Milan Bouchet-Valat <[email protected]> >>>> wrote: >>>> >>>>> Le lundi 22 février 2016 à 06:27 -0800, Jonathan Goldfarb a écrit : >>>>> > I've really been enjoying writing Julia code as a user, and following >>>>> > the language as it develops, but I have noticed that over time, >>>>> > previously fast code sometimes gets slower, and (impressively) >>>>> > previously slow code will sometimes get faster, with updates to the >>>>> > Julia codebase. >>>>> Code is not supposed to get slower with newer releases. If this >>>>> happens, please report the problem here or on GitHub (if possible with >>>>> a reproducible example). This will be very helpful to help avoiding >>>>> regressions. >>>>> >>>>> > No complaint here in general; I really appreciate the work all of the >>>>> > Core and package developers do, and variations in performance of >>>>> > different codes it to be expected. >>>>> > My question is this: has anyone in the Julia community thought about >>>>> > updated performance tips for writing high performance code? >>>>> > Obviously, using the profiler, along with many of the tips >>>>> > at https://github.com/JuliaLang/julia/commits/master/doc/manual/perfo >>>>> > rmance-tips.rst still apply, but I am wondering more about >>>>> > general/structural ideas to keep in mind in Julia v0.4, as well as >>>>> > guidance on how best to take advantage of recent changes on master. I >>>>> > know that document hasn't been stagnant in any sense, but relatively >>>>> > "big in any case, I'd be happy to help make some updates in a PR if >>>>> > there's anything we come up with. >>>>> I've just skimmed through this page, and I don't think any of the >>>>> advice given there is outdated. What's new in master is that anonymous >>>>> functions (and therefore map) are now fast, but that wasn't previously >>>>> mentioned in the tips as a performance issue anyway. >>>>> >>>>> The only small sentence which should likely be removed is "for example, >>>>> currently it’s not possible to infer the return type of an anonymous >>>>> function". Type inference seems to work fine now on master with >>>>> anonymous functions. I'll leave others confirm this. >>>>> >>>>> Anyway, do you have any specific points in mind? >>>>> >>>>> >>>>> Regards >>>>> >>>> >>>> >>
