In this paper, we propose a novel construction for secure distributed matrix multiplication (SDMM) based on algebraic geometry (AG) codes, which we call the PoleGap SDMM scheme. The proposed ...

According to Google DeepMind, AlphaEvolve has successfully discovered multiple new algorithms for matrix multiplication, surpassing the previous AlphaTensor model in efficiency and performance (source ...

Grouped Systematic Matdot Codes for Three-Dimensional Coding of Distributed Matrix Multiplication Large-scale matrix multiplication is a critical operation in various fields such as machine learning, ...

Discover how nvmath-python leverages NVIDIA CUDA-X math libraries for high-performance matrix operations, optimizing deep learning tasks with epilog fusion, as detailed by Szymon Karpiński.

Code samples that demonstrate the algorithms learnt during the course. Used as a preparation (not only ;)) for the exam. java huffman-coding kmp-algorithm matrix-chain-multiplication ...

Researchers upend AI status quo by eliminating matrix multiplication in LLMs Running AI models without floating point matrix math could mean far less power consumption.

algorithms New Breakthrough Brings Matrix Multiplication Closer to Ideal By eliminating a hidden inefficiency, computer scientists have come up with a new way to multiply large matrices that’s faster ...

What do encrypted messages, recognizing speech commands and running simulations to predict the weather have in common? They all rely on matrix multiplication for accurate calculations. DeepMind, an ...

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.

Can artificial intelligence (AI) create its own algorithms to speed up matrix multiplication, one of machine learning’s most fundamental tasks? Today, in a paper published in Nature, DeepMind ...