you can compare the generated code yourself - http://blog.leahhanson.us/julia-introspects.html
no real idea why it would be faster myself. the immutable is passed by value, so you're still pushing data to the stack. would have guessed it to be the same, but am no expert. andrew On Sunday, 28 June 2015 21:19:41 UTC-3, Andrew wrote: > > In the interest of writing abstract code that I could modify easily > depending on the economics model I need, I decided to pack the parameters > of my utility function into a special type. Here's the old function. > > function u(UF::CRRA,a::Float64,aprime::Float64,y::Float64,r::Float64,w:: > Float64) > consump = w*y + (1+r)*a - aprime > u(UF,consump,1) > end > (note: I have tried this with and without the Float64 type annotations. It > makes no difference.) > > and the setup for the new function > > abstract State > immutable State1 <: State > a::Float64 > aprime::Float64 > y::Float64 > r::Float64 > w::Float64 > end > > function u(UF::CRRA,state::State1) > w = state.w > r = state.r > y = state.y > a = state.a > aprime = state.aprime > consump = w*y + (1+r)*a - aprime > u(UF,consump,1) > end > > This function is called within a tight inner loop. Here's the old and new > version. Umatrix_computed is a Bool array. > > Umatrix_computed[i,j,k] ? nothing : ( Umatrix_computed[i,j,k] = true ; > Umatrix[i,j,k] = u(UF, x_grid[i] ,a_grid[j] ,yvals[k] , r , w) ) > > state = State1(x_grid[i] ,a_grid[j] ,yvals[k], r, w) > Umatrix_computed[i,j,k] ? nothing : ( Umatrix_computed[i,j,k] = true ; > Umatrix[i,j,k] = u(UF, state) ) > > Given that I was adding an extra layer of abstraction, I expected this > would be perhaps slightly slower. Instead, the new version runs about 20% > faster (1.2s vs 1s). > > I really don't understand what's going on here. Have I maybe addressed > some type-instability problem? I don't think so, since the original > function had type annotations. Does Julia for some reason find it easier to > pass 1 variable instead of 5? > > Any ideas? Thanks. >
