High-performance matrix multiplication remains a cornerstone of numerical computing, underpinning a wide array of applications from scientific simulations to machine learning.

Matrix multiplication is an important tool for research questions with many variables. The algorithm performs parallel matrix multiplication with the ability to recover from node failures.

The team designed a fully dynamic APSP algorithm in the MPC model with low round complexity that is faster than all the existing static parallel APSP algorithms.

DeepMind breaks 50-year math record using AI; new record falls a week later AlphaTensor discovers better algorithms for matrix math, inspiring another improvement from afar.

Nearly all big science, machine learning, neural network, and machine vision applications employ algorithms that involve large matrix-matrix multiplication. But multiplying large matrices pushes the ...

AI computations, such as those performed by Convolutional Neural Networks (CNNs) or transformer models used in large language models, rely heavily on parallel matrix multiplication.