def set_num_threads(nthreads): """ Sets a number of threads to be used in operations. DEPRECATED: returns the previous setting for the number of threads. During initialization time NumExpr sets this number to the number of detected cores in the system (see `detect_number_of_cores()`). """ old_nthreads = _set_num_threads(nthreads) return old_nthreads
def set_num_threads(nthreads): """ Sets a number of threads to be used in operations. Returns the previous setting for the number of threads. During initialization time Numexpr sets this number to the number of detected cores in the system (see `detect_number_of_cores()`). If you are using Intel's VML, you may want to use `set_vml_num_threads(nthreads)` to perform the parallel job with VML instead. However, you should get very similar performance with VML-optimized functions, and VML's parallelizer cannot deal with common expresions like `(x+1)*(x-2)`, while Numexpr's one can. """ old_nthreads = _set_num_threads(nthreads) return old_nthreads