Skip to content

Instantly share code, notes, and snippets.

Embed
What would you like to do?
A Speed Comparison Of C, Julia, Python, Numba, and Cython on LU Factorization
// lu.c
inline int _(int row, int col, int rows){
return row*rows + col;
}
void det_by_lu(double *y, double *x, int N){
int i,j,k;
*y = 1.;
for(k = 0; k < N; ++k){
*y *= x[_(k,k,N)];
for(i = k+1; i < N; ++i){
x[_(i,k,N)] /= x[_(k,k,N)];
}
for(i = k+1; i < N; ++i){
#pragma omp simd
for(j = k+1; j < N; ++j){
x[_(i,j,N)] -= x[_(i,k,N)] * x[_(k,j,N)];
}
}
}
}
function det_by_lu(y, x, N)
y[1] = 1.
for k = 1:N
y[1] *= x[k,k]
for i = k+1:N
x[i,k] /= x[k,k]
end
for j = k+1:N
for i = k+1:N
x[i,j] -= x[i,k] * x[k,j]
end
end
end
end
function run_julia(y,A,B,N)
loops = max(10000000 // (N*N), 1)
print(loops)
for l in 1:loops
B[:,:] = A
det_by_lu(y, B, N)
end
end
y = [0.0]
N=5
A = rand(N,N)
B = zeros(N,N)
@time run_julia(y,A,B,N)
N=5
A = rand(N,N)
B = zeros(N,N)
@time run_julia(y,A,B,N)
N=10
A = rand(N,N)
B = zeros(N,N)
@time run_julia(y,A,B,N)
N=30
A = rand(N,N)
B = zeros(N,N)
@time run_julia(y,A,B,N)
N=100
A = rand(N,N)
B = zeros(N,N)
@time run_julia(y,A,B,N)
N=200
A = rand(N,N)
B = zeros(N,N)
@time run_julia(y,A,B,N)
N=300
A = rand(N,N)
B = zeros(N,N)
@time run_julia(y,A,B,N)
N=400
A = rand(N,N)
B = zeros(N,N)
@time run_julia(y,A,B,N)
N=600
A = rand(N,N)
B = zeros(N,N)
@time run_julia(y,A,B,N)
N=1000
A = rand(N,N)
B = zeros(N,N)
@time run_julia(y,A,B,N)
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
@dilawar

This comment has been minimized.

Copy link

commented Dec 31, 2016

I would be cool to see how PyPy performs.

@unyty

This comment has been minimized.

Copy link

commented Mar 20, 2018

A perfect exercise for beginners

@t184256

This comment has been minimized.

Copy link

commented May 6, 2018

But why do you compile Cython code without optimizations?

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
You can’t perform that action at this time.