Python multiprocessing memory usage

Question

I have writen a program that can be summarized as follows:

def loadHugeData():
    #load it
    return data

def processHugeData(data, res_queue):
    for item in data:
        #process it
        res_queue.put(result)
    res_queue.put("END")

def writeOutput(outFile, res_queue):
    with open(outFile, 'w') as f
        res=res_queue.get()
        while res!='END':
            f.write(res)
            res=res_queue.get()

res_queue = multiprocessing.Queue()

if __name__ == '__main__':
    data=loadHugeData()
    p = multiprocessing.Process(target=writeOutput, args=(outFile, res_queue))
    p.start()
    processHugeData(data, res_queue)
    p.join()

The real code (especially writeOutput()) is a lot more complicated. writeOutput() only uses these values that it takes as its arguments (meaning it does not reference data)

Basically it loads a huge dataset into memory and processes it. Writing of the output is delegated to a sub-process (it writes into multiple files actually and this takes a lot of time).
So each time one data item gets processed it is sent to the sub-process trough res_queue which in turn writes the result into files as needed.

The sub-process does not need to access, read or modify the data loaded by loadHugeData() in any way. The sub-process only needs to use what the main process sends it trough res_queue. And this leads me to my problem and question.

It seems to me that the sub-process gets its own copy of the huge dataset (when checking memory usage with top). Is this true? And if so then how can i avoid id (using double memory essentially)?

I am using Python 2.6 and program is running on linux.

Asked By: FableBlaze

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Source

Answer 1

The multiprocessing module is effectively based on the fork system call which creates a copy of the current process. Since you are loading the huge data before you fork (or create the multiprocessing.Process), the child process inherits a copy of the data.

However, if the operating system you are running on implements COW (copy-on-write), there will only actually be one copy of the data in physical memory unless you modify the data in either the parent or child process (both parent and child will share the same physical memory pages, albeit in different virtual address spaces); and even then, additional memory will only be allocated for the changes (in pagesize increments).

You can avoid this situation by calling multiprocessing.Process before you load your huge data. Then the additional memory allocations will not be reflected in the child process when you load the data in the parent.

Edit: reflecting @Janne Karila’s comment in the answer, as it is so relevant: "Note also that every Python object contains a reference count that is modified whenever the object is accessed. So, just reading a data structure can cause COW to copy."

Answered By: isedev

Answer 2

As recommended in the documentation (-> Explicitly pass resources to child processes), I would make the large data (explicitly) global, so if there is copy-on-write (COW) available and you fork new processes (in macOS it is spawn by default nowadays), the data is available in the child processes:

def loadHugeData():
    global data
    return data

def processHugeData(res_queue):
    global data
    for item in data:
        res_queue.put(result)
    res_queue.put("END")

But keep in mind, that Python data structures are copied. You would need to some more low-level data types such as numpy because of Python GIL.

Answered By: Michael Dorner

Python multiprocessing memory usage

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