3 ms·

this is just false. you can not find reasonably well written Julia code that runs 10x slower than equivalent fortran.

cycomanic 5y ago
https://jochenschroeder.com/blog/articles/DSP_with_Python2/ https://jochenschroeder.com/blog/articles/DSP_with_Python2/ There the "naive" Julia code, simply implementing the code like I would in Fortran is a factor of 10 or 15 slower than the optimised cython version (which would be the same as a regular C version), the optimised Julia version is still a factor of 5 slower than the cython and pythran version. Can you show me how to optimise it so that Julia performs on par with pythran or cython?
- adgjlsfhk1 5y ago
  The naive Julia code made a few pretty fundamental mistakes (Complex vs Complex{Float64}, and row vs column major). The following is non-optimized Julia code that is roughly 6x faster (and much simpler) than the "optimized" code in the blogpost. Some further optimizations would give another 2-4x over this (like using StaticArrays), but I'll leave that as an exercise for the reader. apply_filter(x, y) = vec(y)' \* vec(x) function cma!(wxy, E, mu, R, os, ntaps) L, pols = size(E) N = (L ÷ os ÷ ntaps - 1) \* ntaps # ÷ or div are integer division err = similar(E) # allocate array without initializing its values @inbounds for k in axes(E, 2) # avoid assuming 1-based arrays. Just need a single inbounds macro call @views for i in 1:N # everything in this block is a view X = E[i*os-1:i*os+ntaps-2, :] Xest = apply_filter(X, wxy[:,:, k]) err[i,k] = (R - abs2(Xest)) \* Xest # abs2 avoids needless extra work wxy[:,:,k] .+= (mu \* conj(err[i,k])) .\* X # remember the dots! end end return wxy, err # note order of returns, seems more idiomatic end

https://jochenschroeder.com/blog/articles/DSP_with_Python2/ https://jochenschroeder.com/blog/articles/DSP_with_Python2/ There the "naive" Julia code, simply implementing the code like I would in Fortran is a factor of 10 or 15 slower than the optimised cython version (which would be the same as a regular C version), the optimised Julia version is still a factor of 5 slower than the cython and pythran version. Can you show me how to optimise it so that Julia performs on par with pythran or cython?

adgjlsfhk1 5y ago
The naive Julia code made a few pretty fundamental mistakes (Complex vs Complex{Float64}, and row vs column major). The following is non-optimized Julia code that is roughly 6x faster (and much simpler) than the "optimized" code in the blogpost. Some further optimizations would give another 2-4x over this (like using StaticArrays), but I'll leave that as an exercise for the reader. apply_filter(x, y) = vec(y)' \* vec(x) function cma!(wxy, E, mu, R, os, ntaps) L, pols = size(E) N = (L ÷ os ÷ ntaps - 1) \* ntaps # ÷ or div are integer division err = similar(E) # allocate array without initializing its values @inbounds for k in axes(E, 2) # avoid assuming 1-based arrays. Just need a single inbounds macro call @views for i in 1:N # everything in this block is a view X = E[i*os-1:i*os+ntaps-2, :] Xest = apply_filter(X, wxy[:,:, k]) err[i,k] = (R - abs2(Xest)) \* Xest # abs2 avoids needless extra work wxy[:,:,k] .+= (mu \* conj(err[i,k])) .\* X # remember the dots! end end return wxy, err # note order of returns, seems more idiomatic end