Implemented the conventional and Strassen's matrix multiplication algorithms for 𝑛 × 𝑛 matrices and determined the optimal cross-over point both analytically and experimentally. For 𝑛 × 𝑛 matrices ...

To run the algorithm you should have a "input.txt" file in the directory where you are running the python scripts. It took me so long to figure out why Strassen takes longer implementation time than ...

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 at MIT's Computer Science & Artificial Intelligence Lab (CSAIL) have open-sourced Multiply-ADDitioN-lESS (MADDNESS), an algorithm that speeds up machine learning using approximate matrix m ...

This algorithm does not require multiply-add operations and speeds up ML by employing approximate matrix multiplication (AMM). MADDNESS runs 10x quicker than other approximation algorithms and 100x ...

We provide a novel approach to the design of fast algorithms for matrix multiplication. The operation of matrix multiplication is reformulated as a convolution, which is implemented using ...

Reducing the number of single operation during matrix-vector multiplication is a method of accelerating of multiplication and decreasing power consumption. It is often not a simple task. The paper ...

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.