Abstract: Contemporary GPU architectures integrate specialized computing units for matrix multiplication, named matrix multiplication units (MXUs), to effectively process neural network applications.

Discovering faster algorithms for matrix multiplication remains a key pursuit in computer science and numerical linear algebra. Since the pioneering contributions of Strassen and Winograd in the late ...

/// @brief Module for handling the matrix-vector multiplication as a part of solving the 1d PDE for heat diffusion. /// Options are: /// 1. 'manual' : using explicit triple loop for matrix-vector ...

In this assignment, you'll be investigating the performance impacts of different cache architectures and different algorithm designs on matrix multiplication. The goals of this assignment are: Show ...

Large language models such as ChaptGPT have proven to be able to produce remarkably intelligent results, but the energy and monetary costs associated with running these massive algorithms is sky high.

Presenting an algorithm that solves linear systems with sparse coefficient matrices asymptotically faster than matrix multiplication for any ω > 2. Our algorithm can be viewed as an efficient, ...

A matrix is a rectangular array of numbers, symbols, or expressions arranged in rows and columns. They are a crucial part of linear algebra and have various applications in fields like engineering, ...