This project implements parallel matrix multiplication using CUDA in Python. It leverages the power of GPU computing to significantly speed up the computation compared to traditional CPU-based methods ...

Code optimized and tuned for one type of GPUs is unlikely to achieve the performance potential on another type of GPUs. Auto-tuners have traditionally been an answer to this performance portability ...

This section presents a total of five approaches, including three matrix multiplication designs applicable to the Baseline, one optimized multiplication design, and one verification method for ...

Distributed computing has markedly advanced the efficiency and reliability of complex numerical tasks, particularly matrix multiplication, which is central to numerous computational applications ...

The ever-increasing demand for matrix multiplication in artificial intelligence (AI) and generic computing emphasizes the necessity of efficient computing power accommodating both floating-point (FP) ...

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 ...

Matrix multiplication and this problem involving tensors are equivalent to each other in a sense, yet researchers already had faster procedures for solving the latter one.

The matrix multiplication infix operator (*) produces a new matrix by performing matrix multiplication. The first matrix must have the same number of columns as the second matrix has rows.