![]() ![]() Synchronization between processes must be manually handled.Potentially faster I/O performance by writing sequentially to disk instead of appending to a Queue.Objects are written once to disk instead of being copied into the Queue. Avoiding the memory copying that occurs with Queues.Writing to disk avoids serializing the objects. Avoiding the pickling and unpickling overhead for large objects.Writing directly to the hard drive to communicate between processes instead of using multiprocessing Queues in Python has some pros and cons: As we can see the status of each sub-process is S meaning that it is sleeping. It is more than 20x slower even if the computations are exactly the same. This runs on my computer in approximately 145 seconds. Replace the line where we put a 1 in the queue by : q.put(np.zeros((1500, 1500, 3)) Let’s put a heavy numpy array in the queue instead. As we can see the status of each sub-process is R meaning that it is running. This runs on my computer in approximately 7 seconds. P = mp.Process(target=heavy_function, args=(n,)) All of the intensive computation (the matrix multiplication) is exactly the same regardless of what we put in the queue. The only difference between our two experiments is the size of the objects shared through the queue. Multiprocessing experiment highlighting the problem If necessary, one approach is to share memory objects using the multiprocessing.sharedctypes module instead of pickling, which avoids the overhead of pickling and unpickling. To improve performance, it is best to avoid sharing large objects over multiprocessing queues when possible. For large objects, creating copies can be very expensive in terms of time and memory. Memory copying: Multiprocessing queues create copies of objects when they are transferred between processes.Since multiprocessing queues use locks to safely transfer objects between processes, the GIL can limit performance when transferring large objects that require acquiring the lock for long periods of time. This lock is necessary mainly because Python’s memory management is not thread-safe. GIL limitations: The Global Interpreter Lock (GIL) prevents multiple native threads from executing Python byte-codes at once. ![]() This is because large objects require more data to be pickled and transferred, and the unpickling step requires reconstructing the entire object. The pickling and unpickling steps add overhead, and for large objects this overhead can be significant. Pickling and unpickling: Objects placed on multiprocessing queues are pickled, transferred over the queue, and then unpickled.There are a few reasons why this may be the case: However, these queues can become slow when large objects are being shared between processes. Multiprocessing queues in Python allow multiple processes to safely exchange objects with each other. ![]()
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