A high-performance implementation of matrix multiplication using Strassen’s algorithm and OpenMP-based parallelization. Developed as part of a Parallel Computing course to explore recursive algorithms ...

Abstract: Convolution neural networks (CNNs) have been extensively used in machine learning applications. The most time-consuming part of CNNs are convolution operations. A common approach to ...

This project demonstrates a modern parallel computing workflow using the Taskflow C++17 library. It implements tile-based (blocked) matrix multiplication and matrix addition inside a task dependency ...

Algorithms have been used throughout the world’s civilizations to perform fundamental operations for thousands of years. However, discovering algorithms is highly challenging. Matrix multiplication is ...

Abstract: This paper introduces a useful technique which can be used in a parallel matrix multiplication with the tiling method. Firstly, we exploit the effect of the matrix transpose for the tiling ...

High-performance matrix multiplication remains a cornerstone of numerical computing, underpinning a wide array of applications from scientific simulations to machine learning. Researchers continually ...

With AlphaTensor, DeepMind Technologies has presented an AI system that is supposed to independently find novel, efficient and provably correct algorithms for complex mathematical tasks. AlphaTensor ...

Computer scientists are a demanding bunch. For them, it’s not enough to get the right answer to a problem — the goal, almost always, is to get the answer as efficiently as possible. Take the act of ...

Mathematicians love a good puzzle. Even something as abstract as multiplying matrices (two-dimensional tables of numbers) can feel like a game when you try to find the most efficient way to do it.