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.

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.

Abstract Can linear systems be solved faster than matrix multiplication? While there has been remarkable progress for the special cases of graph structured linear systems, in the general setting, the ...

Matrix multiplication advancement could lead to faster, more efficient AI models At the heart of AI, matrix math has just seen its biggest boost "in more than a decade.” ...

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 ...

Dynamic Programming (DP) problems arise in wide range of application areas spanning from logistics to computational biology. In this paper, we show how to obtain high-performing parallel ...

Multiplying Matrices Matrix multiplication is one of the most fundamental and ubiquitous operations in all of mathematics. To multiply a pair of n -by- n matrices, each with n2 elements, you multiply ...

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 ...