Doubling the Performance of Python/NumPy with less than 100 SLOC

A very simple, and outside NumPy, commonly used trick of buffer-reuse is introduced to the NumPy library to speed up the performance of scientific applications in Python/NumPy. The implementation, which we name software victim-caching, is very simple. The code itself consists of less than 100 lines of code, and took less than one day to add to NumPy, though it should be noted that the programmer was very familiar with the inner workings of NumPy. The result is an improvement of as much as 2.29 times speedup, on average 1.32 times speedup across a benchmark suite of 15 applications, and at no time did the modification perform worse than unmodified NumPy.