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.

We propose an efficient quantum subroutine for matrix multiplication that computes a state vector encoding the entries of the product of two matrices in superposition. The subroutine exploits ...

Java’s collections like arrays and lists are foundational building blocks. Functional programming techniques are at times the ideal way to work with these collections.

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.” ...

Now the task of hastening the process of matrix multiplication lies at the intersection of mathematics and computer science, where researchers continue to improve the process to this day — though in ...

Achieving high performance for Sparse Matrix-Matrix Multiplication (SpMM) has received increasing research attention, especially on multi-core CPUs, due to the large input data size in applications ...

An artificial-intelligence approach known as AlphaTensor found exact matrix-multiplication algorithms that are more efficient than those previously known for many matrix sizes. The technique ...

This small project demonstrates how to use concurrent programming to implement a matrix multiplication in java - ArieGru/matrix_multipication-20554_course ...