multiprocessing shows no benefit

[Terry Reedy]

On 10/17/2017 10:52 AM, Jason wrote:
> I've got problem that I thought would scale well across cores.

What OS?

> def f(t):
>     return t[0]-d[ t[1] ]
> 
> d= {k: np.array(k) for k in entries_16k }
> e = np.array()

> pool.map(f, [(e, k) for k in d]

*Every* multiprocessing example in the doc intentionally protects 
multiprocessing calls with

if __name__ == '__main__':

"Safe importing of main module

     Make sure that the main module can be safely imported by a new 
Python interpreter without causing unintended side effects (such a 
starting a new process)."

> At the heart of it is a list of ~16k numpy arrays (32 3D points) which are stored in a single dict.  Using pool.map() I pass the single item of 32 3D Points to  be evaluated again the 16k entries. In theory, this would be a speedup proportional to the number of physical cores, but I see all 4 cores being maxed out and results in a regular map time.
> 
> How can I use pool.map better?

Try following the doc and see what happens.

-- 
Terry Jan Reedy