I’m attempting to go full Julia and not store functions in a type. The meat of my problem requires doing a some quadrature using QuadGK.jl on the product of some bessel functions using SpecialFunctions.jl and a function or it’s derivative. I’m always able to take the derivative by hand so I don’t rely on automatic differentiation. Please see this gist which contains a MWE with enough complexity to represent the broader problem.
The gist contains four files
Main.jl; to run the code do
julia Main.jlfrom e.g. the terminal
Work.jl; where the calculations are actually performed
Objects.jl; contains my OOP approach
Functionals.jl; contains what I hope is a Julian approach
I need to do some complicated operations deep down in my source code and have them configurable by the user. I have coded up an OOP approach which stores complicated functions in a
type and passing that as an argument; see
Objects.jl. For a more Julian approach I create a concrete
type and make it callable with different arguments from inside
Main.jl. I’ve also written a more hard coded approach which is in
BenchmarkTools gives me some strange results. The OOP and hard-coded approaches run in the same amount of time while my Julian attempt takes 2-3 times longer. The Julian attempt does far more allocations too.
Any comments and improvements are welcome!
My version info:
julia> versioninfo() Julia Version 0.6.4-pre.2 Commit 003c43eed2* (2018-06-05 13:07 UTC) Platform Info: OS: macOS (x86_64-apple-darwin17.5.0) CPU: Intel(R) Core(TM) i7-5557U CPU @ 3.10GHz WORD_SIZE: 64 BLAS: libopenblas (USE64BITINT DYNAMIC_ARCH NO_AFFINITY Haswell MAX_THREADS=16) LAPACK: libopenblas64_ LIBM: libopenlibm LLVM: libLLVM-3.9.1 (ORCJIT, broadwell)