How to use the 'wide' crate for portable SIMD programming in stable Rust, with a Mandelbrot set implementation example.
A guide to using SIMD (SSE) instructions in C# to optimize performance, demonstrated by summing a list of integers.
Explains efficient vectorized methods for sampling points from spline curves and 2-sphere splines using linear algebra and caching techniques.
Explores using SIMD (Single Instruction, Multiple Data) CPU instructions to optimize the Levenshtein Distance string comparison algorithm.
A guide to implementing multivariate linear regression with gradient descent in JavaScript, including feature scaling.
A guide to implementing vectorized gradient descent in JavaScript for machine learning, improving efficiency over unvectorized approaches.
R 3.4.0 is released with major performance upgrades including JIT compiler enabled by default, faster matrix operations, and improved vector handling.
