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Christopher Ariza - When and How to Try with Your Own C-Extensions | SciPy 2023
Learn when and how to try C extensions with your Python code to improve performance, including using C types, sliding window approaches, and SIMD, and discover the benefits and opportunities for optimization.
- C extensions can be used to improve performance when working with Python code.
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Consider using C types instead of Python objects (e.g.,
piobjects) when working with arrays. -
np.argmaxis two orders of magnitude faster than a simple C implementation. - Sliding window approach can be used to find the first true value in a boolean array.
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Using
piarray operations (e.g.,piarray get pointer) can be faster than equivalent Python code. - SIMD can be used to improve performance, especially with modern CPUs.
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numpyroutines can be slow when dealing with large arrays, and C extensions can be used to improve performance. - Good opportunities for improving performance include using C extensions, avoiding Python objects, and optimizing the inner loop.
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npyiter provides a common iteration interface in C and supports everything from iterators to arrays. -
simdreduces loop iteration, making the code faster. -
numpyhas a lot of flexibility, but also a lot of overhead, which can affect performance. - C extensions can be useful for improving performance, especially in cases where the work can be done using C types.
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piarray operations can be used to improve performance, especially when working with large arrays. - Iterating over the array in reverse is essentially the same as iterating forward.