I am using
Eigenvalues a large number of times on a machine with 8 cores. When I run
Eigenvalues one time without any parallel implementation, the CPU usage hovers around 50%. I suppose there is some parallelization built into the function.
Ideally, if I had enough memory, I would launch 8 cores and use ParallelMap: this gives 100% CPU usage. However, I quickly run out of memory.
I do have enough memory to launch two cores and run in parallel. However, now the CPU usage is 25%, so this is slower than simple sequential evaluation. It seems using
ParallelMap has excluded the in-built parallelization of
Is there any way I can get 2 parallel cores to run
Eigenvalues, each one taking advantage of the parallelization already built into the function? i.e. any way I can use the extra memory I have to get 100% out of my CPUs?