Learn even more techniques for reducing memory usage-read the rest of the Larger-than-memory datasets guide for Python. Just make sure not to modify the original array you may have unexpected consequences depending on your operating system.įor other data structures, like dictionaries or lists, you can use immutable datastructures to reduce memory usage of mostly-similar copies in Python the pyrsistent library is one implementation. In this situation, copy-on-write saves memory by only allocating memory for data that has actually changed. You are making copies and only partially modifying those copies.As the array size increases, Numpy is able to execute more parallel operations and. The mmap() copy-on-write trick is useful when: As array size gets close to 5,000,000, Numpy gets around 120 times faster. Then you Google, and within 2 minutes you are done I love this types of articles. Need to identify the memory and performance bottlenecks in your own Python data processing code? Try the Sciagraph profiler, with support for profiling both in development and production macOS and Linux, and with built-in Jupyter support. Sometimes you think you’re going to spend at least 20 minutes researching a subject. Note: Whether or not any particular tool or technique will help depends on where the actual memory bottlenecks are in your software. page1b page2b page2b->page2 page3b page3b->page3 page4b. G cluster_array1 Array 1 cluster_array2 Array 2 page1 page2 page3 page4. The _memmap() function will open a file of the appropriate size we’ll start by creating our initial array: Note: On Linux you can go one step further and create an in-memory file using the memfd_create API, which can be used in Python 3.8 and later by doing os.fdopen(os.memfd_create("mymemfile"), "rb+") and then “truncating” the file to be the appropriate size. While there is a file involved, so long as there’s enough memory available the file is almost an implementation detail it needs to be there but it won’t impact performance much. The main difference between a copy and a view of an array is that the copy is a new array, and the view is just a view of the original array. To use mmap() in this mode, we need a backing file. If you’re not familiar with mmap(), see my overview comparing mmap() with HDF5 and Zarr. In an ideal world, that second array would only store the differences from the first array: insofar as differences are few, the additional memory usage would be small.Īnd that’s where mmap()’s copy-on-write functionality comes in (or the equivalent API on Windows NumPy wraps them both). NumPy implements the multidimensional array structure in C and provides a convenient Python interface, thus bringing together high performance and ease of use. Thus the original array is not copied in memory. The pages are chunks of 4KB that are the unit of memory management for the operating system. A slicing operation creates a view on the original array, which is just a way of accessing array data.
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