As Nvidia marks two decades of CUDA, its head of high-performance computing and hyperscale reflects on the platform’s journey ...

Abstract: Even though the task of multiplying matrices appears to be rather straightforward, it can be quite challenging in practice. Many researchers have focused on how to effectively multiply two 2 ...

TPUs are Google’s specialized ASICs built exclusively for accelerating tensor-heavy matrix multiplication used in deep learning models. TPUs use vast parallelism and matrix multiply units (MXUs) to ...

The inspiration for this column comes not from the epic 1999 film The Matrix, as the title may suggest, but from an episode of Sean Carroll’s Mindscape podcast that I listened to over the summer. The ...

A robust and user-friendly scientific calculator application built with Python's Tkinter for the graphical interface and NumPy for powerful numerical and matrix operations. This project aims to ...

While we have the Python built-in function sum() which sums the elements of a sequence (provided the elements of the sequence are all of numeric type), it’s instructive to see how we can do this in a ...

Abstract: Linear systems involved in engineering and scientific calculations can be more easily analyzed using similarity transformation. However, understanding the numerous abstract linear algebra ...

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.