Perhaps not very surprising, but the setting the number of workers to mp.cpu_count() when parallel=True does not yield optimal performance. From testing on different users computers, results were a little hard to interpret, for some it worked fine with parallel=True, for others the computation slowed down compared to the single core computation. Overall though, I think it would better to default to mp.cpu_count() // 2 or maybe even a bit less as the number of workers, which should deliver a speedup for most people.
Perhaps not very surprising, but the setting the number of workers to
mp.cpu_count()whenparallel=Truedoes not yield optimal performance. From testing on different users computers, results were a little hard to interpret, for some it worked fine with parallel=True, for others the computation slowed down compared to the single core computation. Overall though, I think it would better to default tomp.cpu_count() // 2or maybe even a bit less as the number of workers, which should deliver a speedup for most people.