Actually I suppose normalizing by calls to integrand does answer your point about implementations*, *just about algorithm. Its true, when I look at the ProfileView (attached) it does seem like most of the time is actually spent inside quadgk. In fact, most of it is inside the jl_apply_generic function. I don't know enough about Julia to know what that function does. Could that be a sign there's something non-optimal going on? (To profile this I am doing @profile for _=1:10000; test.f(1.); end to get enough samples, is that correct?)
Marius On Sunday, June 19, 2016 at 4:41:47 PM UTC+2, Marius Millea wrote: > > They *are* different algorithms, but when I was comparing speeds with the > other codes, I compared it in terms of time per number of calls of the > inner integrand function. So basically I'm testing the speed of the > integrand functions themselves, as well as the speed of the integration > library code, as well as any function call overhead type thing. With this > metric, the Julia code was close, but it was the slowest (although of > course far more succinct and easy to read). > > > On Saturday, June 18, 2016 at 7:46:35 PM UTC+2, Gabriel Gellner wrote: >> >> What integration library are you using with Cython/Fortran? Is it using >> the same algorithm as quadgk? Your code seems so simple I imagine this is >> just comparing the quadrature implementations :) >> >> On Saturday, June 18, 2016 at 5:53:57 AM UTC-7, Marius Millea wrote: >>> >>> Hi all, I'm sort of just starting out with Julia, I'm trying to get >>> gauge of how fast I can make some code of which I have Cython and Fortran >>> versions to see if I should continue down the path of converting more or my >>> stuff to Julia (which in general I'd very much like to, if I can get it >>> fast enough). I thought maybe I'd post the code in question here to see if >>> I could get any tips. I've stripped down the original thing to what I think >>> are the important parts, a nested integration with an inner function >>> closure and some global variables. >>> >>> module test >>> >>> const a = 1. >>> >>> function f(x) >>> quadgk(y->1/g(y),0,x)[1] # <=== outer integral >>> end >>> >>> function g(y) >>> integrand(x) = x^2*sqrt(x^2*y^2+a)/(exp(sqrt(x^2+y^2))+a) >>> quadgk(integrand,0,Inf)[1] # <=== inner integral >>> end >>> >>> end >>> >>> >>> > @timeit test.f(1.) >>> 100 loops, best of 3: 3.10 ms per loop >>> >>> >>> >>> >>> Does anyone have any tips that squeezes a little more out of this code? >>> I have run ProfileView on it, and although I'm not sure I fully understand >>> how to read its output, I think it's saying the majority of runtime is >>> spent in quadgk itself. So perhaps I should look into using a different >>> integration library? >>> >>> Thanks for any help. >>> >>>
