9/11/2023 0 Comments Queue in python 3![]() ![]() # and create a queue to keep track of which ones are freeīuf = Array('c', te_count, lock=False) # make a pool of numpy arrays, each backed by shared memory, # note: every ndarray put on the queue must be this size # find the size and data type for the arrays Raise ValueError('ArrayQueue(template, maxsize) must use a finite value for maxsize.') # this queue cannot be infinite, because it will be backed by real objects Raise ValueError('ArrayQueue(template, maxsize) must use a numpy.ndarray as the template.') from multiprocessing import Process, Queue, Array This has similar performance to the threaded version above (about 10% slower), and may scale better if the global interpreter lock is an issue (i.e., you run a lot of python code in the functions). This is much faster than sending the whole array through the queue, since it avoids pickling the arrays. When a result array is pushed onto the queue, ArrayQueue copies the data from that array into an existing shared-memory array, then passes the id of the shared-memory array through the queue. It creates and manages a pool of numpy arrays backed by shared memory. The ArrayQueue object should be created before spawning subprocesses. The code below creates a new ArrayQueue class to do that. %time print sum(r.sum() for r in rs) # 12.2sĪnother option, close to what you requested, would be to continue using the multiprocessing package, but pass data between processes using arrays stored in shared memory. I have suggested some other approaches further down.) from threading import ThreadĬonsumer_process = Thread(target=consumer,args=(f_xs,q,)) (However, it's only about 20% faster than the serial version of your code. On my computer, this runs about 3 times faster than your code. This passes a numpy ndarray via a queue without pickling it. Here's a version of your code, tweaked to use threading.Thread and Queue.Queue instead of multiprocessing.Process and multiprocessing.Queue. This means you can do parallel processing with standard threads and shared memory, instead of multiprocessing and inter-process communication. Since you're using numpy, you can take advantage of the fact that the global interpreter lock is released during numpy computations. Sharing memory between threads or processes Use threading instead of multiprocessing from multiprocessing import Process, QueueĬonsumer_process = Process(target=consumer,args=(f_xs,q,)) (So the global variable approach would be ugly.). Unfortunately the arrays are being generated after the consumer is started, and there is no easy way around that. ![]() Is there any pythonic way to pass references to shared memory instead of pickling the arrays? I have a multiprocessing job where I'm queuing read only numpy arrays, as part of a producer consumer pipeline.Ĭurrently they're being pickled, because this is the default behaviour of multiprocessing.Queue which slows down performance. ![]()
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