This is a short post that explains how to write a high-performance matrix multiplication program on modern processors. In this tutorial I will use a single core of the Skylake-client CPU with AVX2, but the principles in this post also apply to other processors with different instruction sets (such as AVX512).
Matrix multiplication is a mathematical operation that defines the product of
| # use ImageMagick convert | |
| # the order is important. the density argument applies to input.pdf and resize and rotate to output.pdf | |
| convert -density 90 input.pdf -rotate 0.5 -attenuate 0.2 +noise Multiplicative -colorspace Gray output.pdf |
| #pragma once | |
| #include <boost/optional.hpp> | |
| /** | |
| * Wraps any value with a context of Left for the Either class. | |
| * | |
| * By convention, Left represents some sort of less-desired condition. | |
| */ |
Using Python's built-in defaultdict we can easily define a tree data structure:
def tree(): return defaultdict(tree)That's it!
This gist is part of a blog post. Check it out at:
http://jasonrudolph.com/blog/2011/08/09/programming-achievements-how-to-level-up-as-a-developer