Is there a relatively easy way to speed up the computation in Julia? Could this issue be particular to my machine? I’m happy to provide any more needed details. This gives ~2x speed up for the eigen() computation in Julia.
Note that I’ve used the MKL.jl package, which I learned to do from this thread. And in MATLAB: > A = rand(1000, 1000) + 1i * rand(1000, 1000) Īlthough both times are longer, the discrepancy remains for totally random (i.e. One can compare times for the two eigensolvers to diagonalize random Hermitian matrices inĪ = rand(1000, 1000) + im * rand(1000, 1000) My program involves diagonalizing many MxM (Hermitian) matrices, where M ~ 10^2-10^3. I noticed the program in Julia was quite noticeably slower than the MATLAB version and, using the profiler, I discovered that the eigensolver “eigen()” takes about 2x longer than the “eig()” solver in MATLAB. I’ve been learning Julia in the past week and in the process, I have re-written one of my programs, which I previously implemented in MATLAB.
