Are you talking about the incremental GC <https://github.com/JuliaLang/julia/pull/5227>?
It happens that, since I'm making some experiments with a (pseudo-)realtime simulation with Julia, I also have that branch compiled. In my realtime experiment, at the activation of a Timer with a period of 2.2ms, I get a big delay of +/-9ms each +/-1sec. when using master Julia. By using the incremental GC those delays disappear. However, in the time measurements I described before, the use of the incremental GC doesn't seem to produce any better results... On Tuesday, June 17, 2014 5:32:34 PM UTC+1, John Myles White wrote: > > Sounds like we need to rerun these benchmarks after the new GC branch gets > updated. > > -- John > > On Jun 17, 2014, at 9:31 AM, Stefan Karpinski <[email protected] > <javascript:>> wrote: > > That definitely smells like a GC issue. Python doesn't have this > particular problem since it uses reference counting. > > > On Tue, Jun 17, 2014 at 12:21 PM, Cristóvão Duarte Sousa <[email protected] > <javascript:>> wrote: > >> I've just done measurements of algorithm inner loop times in my machine >> by changing the code has shown in this commit >> <https://github.com/cdsousa/Comparison-Programming-Languages-Economics/commit/4f6198ad24adc146c268a1c2eeac14d5ae0f300c> >> . >> >> I've found out something... see for yourself: >> >> using Winston >> numba_times = readdlm("numba_times.dat")[10:end]; >> plot(numba_times) >> >> >> <https://lh6.googleusercontent.com/-m1c6SAbijVM/U6BpmBmFbqI/AAAAAAAADdc/wtxnKuGFDy0/s1600/numba_times.png> >> julia_times = readdlm("julia_times.dat")[10:end]; >> plot(julia_times) >> >> >> <https://lh4.googleusercontent.com/-7iprMnjyZQY/U6Bp8gHVNJI/AAAAAAAADdk/yUgu8RyZ-Kw/s1600/julia_times.png> >> println((median(numba_times), mean(numba_times), var(numba_times))) >> (0.0028225183486938477,0.0028575707378805993,2.4830103817464292e-8) >> >> println((median(julia_times), mean(julia_times), var(julia_times))) >> (0.0028240440000000004,0.0034863882123824454,1.7058255003790299e-6) >> >> So, while inner loop times have more or less the same median on both >> Julia and Numba tests, the mean and variance are higher in Julia. >> >> Can that be due to the garbage collector being kicking in? >> >> >> On Monday, June 16, 2014 4:52:07 PM UTC+1, Florian Oswald wrote: >>> >>> Dear all, >>> >>> I thought you might find this paper interesting: http://economics. >>> sas.upenn.edu/~jesusfv/comparison_languages.pdf >>> >>> It takes a standard model from macro economics and computes it's >>> solution with an identical algorithm in several languages. Julia is roughly >>> 2.6 times slower than the best C++ executable. I was bit puzzled by the >>> result, since in the benchmarks on http://julialang.org/, the slowest >>> test is 1.66 times C. I realize that those benchmarks can't cover all >>> possible situations. That said, I couldn't really find anything unusual in >>> the Julia code, did some profiling and removed type inference, but still >>> that's as fast as I got it. That's not to say that I'm disappointed, I >>> still think this is great. Did I miss something obvious here or is there >>> something specific to this algorithm? >>> >>> The codes are on github at >>> >>> https://github.com/jesusfv/Comparison-Programming-Languages-Economics >>> >>> >>> > >
