Intel SSE and AVX Examples and Tutorials [closed]

For the visually inclined SIMD programmer, Stefano Tommesani’s site is the best introduction to x86 SIMD programming. http://www.tommesani.com/index.php/simd/46-sse-arithmetic.html The diagrams are only provided for MMX and SSE2, but once a learner gets proficient with SSE2, it is relatively easy to move on and read the formal specifications. Intel IA-32 Instructions beginning with A to M …

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Difference between frompyfunc and vectorize in numpy

As JoshAdel points out, vectorize wraps frompyfunc. Vectorize adds extra features: Copies the docstring from the original function Allows you to exclude an argument from broadcasting rules. Returns an array of the correct dtype instead of dtype=object Edit: After some brief benchmarking, I find that vectorize is significantly slower (~50%) than frompyfunc for large arrays. …

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Is it possible to vectorize recursive calculation of a NumPy array where each element depends on the previous one?

You might think this would work: import numpy as np n = len(Tm) t = np.empty(n) t[0] = 0 # or whatever the initial condition is t[1:] = Tm[1:] + (t[0:n-1] – Tm[1:])**(-tau[1:]) but it doesn’t: you can’t actually do recursion in numpy this way (since numpy calculates the whole RHS and then assigns it …

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