Numpy copy fast9/19/2023 ![]() I don't know why cython performance is significantly slower, but this may be due to some compiler flags. For larger arrays it looks like Numpy is switching to parallelized copy, which has to be implemented in Numba manually. It looks like Numba is a tiny bit faster on small arrays, and on par on medium sized arrays. There may be an impact of switching to a parallelized version I'm using my own library simple_benchmark for the performance measurements here: from simple_benchmark import BenchmarkBuilder, MultiArgumentī.add_functions([copyto_cython, copyto_numpy, size') However numba is already faster than similar code with Cython (which I find amazing):Ĭpdef copyto_cython(double in_array, double out_array): Numba excels when you need to write code operating on arrays that is not already implemented in NumPy, SciPy, or any other optimized library. It's unlikely that you can with numba! Often it's 50-200% slower on big arrays if you try to re-implement some native-NumPy functionality with numba and that's already quite close. How can I make Numba access arrays as fast as Numpy can? That should also answer the question title: After all it's comparing hand-written (often highly-optimized) code with auto-generated code. In my experience that's pretty much always the case, except you're willing to sacrifice accuracy ( fastmath - not relevant here because we're not doing any math) or you can utilize multi-threading (in this case probably not worth it because copy is basically memory-bandwidth limited) or multi-processing (as the other answer show this can make it faster for larger arrays). I'm not expecting Numba to be faster but certainly not 70% slower! %%cython -c=-march=native -c=-O3 -c=-ftree-vectorize -c=-flto -c=-fuse-linker-pluginĬimport # Deactivate bounds # Deactivate negative indexing.Ĭpdef copyto_cython_flags(double in_array, double out_array):Ĭhecked CPU usage to confirm Numpy was only using one core.Īgain, I'm not expecting Numba to be faster but certainly not 70% slower! Is there anything I can do to make this faster? Note that np.copyto is not implemented in nopython mode so it gets very slow with small vectors. New version with Cython (as fast as Numba, slower than Numpy, slight variation from answer): %load_ext cython Remove the loop and copy the full array at once: b = a (twice slower!).Change the type of the loop counter to unsigned integer (32 bits and 64 bits).Use fastmath=True and/or nogil=True in case it triggers vectorization.Force use of flattened arrays: b.flat = a.flat.Dummy loop in the functions to "stay longer in no-python mode".of 7 runs, 1000 loops each)Ģ.19 ms ± 222 µs per loop (mean ± std. Timings 1.28 ms ± 5.58 µs per loop (mean ± std. Minimum working example import numpy as np ![]() I'm not expecting Numba to be faster but nearly as fast seems like a reasonable target. Numpy is basically faster at copying array content to another array (or so it seems, for large-enough arrays). ![]()
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