Also, defining mylog(x::Float64) = ccall((:log, "libm"), Float64, (Float64,), x)
made quite a bit of difference for me, from 1.92 to around 1.55. If I also add @inbounds, I go down to 1.45, making Julia only twice as sslow as C++. Numba still beats Julia, which kind of bothers me a bit Thanks for the suggestions. On Monday, June 16, 2014 4:56:34 PM UTC-4, Jesus Villaverde wrote: > > Hi > > 1) Yes, we pre-compiled the function. > > 2) As I mentioned before, we tried the code with and without type > declaration, it makes a difference. > > 3) The variable names turns out to be quite useful because this code will > be eventually nested into a much larger project where it is convenient to > have very explicit names. > > Thanks > > On Monday, June 16, 2014 12:13:44 PM UTC-4, Dahua Lin wrote: >> >> First, I agree with John that you don't have to declare the types in >> general, like in a compiled language. It seems that Julia would be able to >> infer the types of most variables in your codes. >> >> There are several ways that your code's efficiency may be improved: >> >> (1) You can use @inbounds to waive bound checking in several places, such >> as line 94 and 95 (in RBC_Julia.jl) >> (2) Line 114 and 116 involves reallocating new arrays, which is probably >> unnecessary. Also note that Base.maxabs can compute the maximum of absolute >> value more efficiently than maximum(abs( ... )) >> >> In terms of measurement, did you pre-compile the function before >> measuring the runtime? >> >> A side note about code style. It seems that it uses a lot of Java-ish >> descriptive names with camel case. Julia practice tends to encourage more >> concise naming. >> >> Dahua >> >> >> >> On Monday, June 16, 2014 10:55:50 AM UTC-5, John Myles White wrote: >>> >>> Maybe it would be good to verify the claim made at >>> https://github.com/jesusfv/Comparison-Programming-Languages-Economics/blob/master/RBC_Julia.jl#L9 >>> >>> >>> I would think that specifying all those types wouldn’t matter much if >>> the code doesn’t have type-stability problems. >>> >>> — John >>> >>> On Jun 16, 2014, at 8:52 AM, Florian Oswald <florian...@gmail.com> >>> 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 >>> > >>> > >>> >>>
