the performance of the determination of the eigenvalues and the eigenvector for small complex matrices in Julia seems for me quite slow. If I use the following code
N = 100000
dim = 3
for i = 1:N
A = rand(dim, dim) + im * rand(dim, dim)
it takes about 3sec.
3.226431 seconds (5.76 M allocations: 436.489 MiB, 2.34% gc time)
If I use the same code in Python (with numpy) the code is slighty slower (4.4s), however by using numba I can speed up the loop and it takes then only 0.7s which is much faster than Julia.
Is there a workaround or package to enhance the performance for these matrices?
PS: versioninfo in Julia gives the following
Julia Version 0.6.2
Commit d386e40c17* (2017-12-13 18:08 UTC)
OS: Windows (x86_64-w64-mingw32)
CPU: Intel® Core™ i5-6500 CPU @ 3.20GHz
LLVM: libLLVM-3.9.1 (ORCJIT, skylake)