Vectorization is a process in computing where multiple data points are processed simultaneously in a single instruction, rather than one at a time. This is particularly useful in performance-critical applications like scientific computing, data analysis, and machine learning.
In the Context of SIMD (Single Instruction, Multiple Data):
SIMD is a hardware feature that allows a single operation to be applied to multiple pieces of data simultaneously, leveraging vectorized operations. For example:
Instead of adding numbers one by one in a loop, vectorized code can add entire arrays in one operation.
Benefits of Vectorization:
Speed: Reduces the number of instructions the CPU executes.
Efficiency: Exploits modern CPU architectures optimized for SIMD.
Code Simplicity: Simplifies array or matrix operations in high-level languages.
In Python, libraries like NumPy take advantage of vectorization for high-speed computation.