There are many numerical operations that can be performed in scientific computing applications, and non-optimized code or library implementations cause serious performance bottlenecks.
The NumPy library helps to increase the performance of Python programs by using its memory layout more efficiently.
One of the most commonly used branches of mathematics in real-world applications is linear algebra. Linear algebra is used for computer graphics, cryptography, econometrics, machine learning, deep learning, and natural language processing, to name but a few of its uses. Having performant matrix and vector operations is crucial.
High-performance, low-level frameworks, such as BLAS, LAPACK, and ATLAS—which are part of Netlib's libraries, and are used for dense linear algebra operations—and other frameworks...
