Comments (8)
Is this going to be an issue? https://codingnest.com/generating-random-numbers-using-c-standard-library-the-problems/
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Use python's multiprocessing
module to execute runs
from hpr-sim.
numpy.random
for rng... can return numpy arrays of arbitrary shapepyDOE2
for LHS capability
from hpr-sim.
Be careful with numpy.random
; make sure RNG state is properly isolated between processes (import multiprocessing as mp
from hpr-sim.
Use new instance of numpy.random.RandomState()
in each process to isolate RNG state(s)
from hpr-sim.
Managing RNG state across processes, consequences on seeding:
- https://peteroupc.github.io/random.html#Seeding_Multiple_Processes
- https://numpy.org/doc/stable/reference/random/parallel.html
- https://stackoverflow.com/questions/59263562/impact-of-setting-random-seed-to-recreate-a-simulated-behaviour-and-choosing-t
from hpr-sim.
Passing multiple arguments into map
for Pool:
- https://stackoverflow.com/questions/5442910/how-to-use-multiprocessing-pool-map-with-multiple-arguments
- https://stackoverflow.com/questions/25553919/passing-multiple-parameters-to-pool-map-function-in-python?noredirect=1&lq=1
Good solution are those above, in tandem with functools.partial
:
from hpr-sim.
Use Philox
in numpy.random
to generate independent, uncorrelated streams: https://numpy.org/doc/stable/reference/random/parallel.html#independent-streams
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Related Issues (20)
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