BLAS
BLAS (Basic Linear Algebra Subprograms) is a standardized library of low-level routines for performing common linear algebra operations, such as vector and matrix multiplication. It provides efficient, portable implementations that serve as the foundation for many scientific computing and machine learning libraries. BLAS is widely used in high-performance computing to optimize numerical computations.
Developers should learn BLAS when working on performance-critical applications involving linear algebra, such as scientific simulations, data analysis, or machine learning models. It is essential for optimizing numerical code in languages like Python (via NumPy), R, or C/C++, as it leverages hardware-specific optimizations like SIMD instructions and multi-threading. Using BLAS can significantly speed up matrix operations compared to naive implementations.