Multi-threading

In order to accelerate the computation via multi-threading parallel, please make sure Julia is started with multiple threads. A warning will be thrown to remind you when loading FiniteMPS with a single thread.

If the linear algebra backend is OpenBlas, nested multi-threading is forbidden (a related discussion here). So just close the parallelism of BLAS via

BLAS.set_num_threads(1)

When using MKL as the linear algebra backend, you can set the number of blas threads similarly, in which case nested multi-threading is allowed. Note MKL is invalid for some cpus and you should also close the BLAS parallelism in such case. You can follow MKL.jl to check if MKL is loaded successfully or not.

Please make sure the total threads number is not larger than the cpu cores (physical, without hyper-threading). Otherwise, the performance will become much worse due to conflict. The total threads can be estimated as

Threads.nthreads() * BLAS.get_num_threads()