CUDA-L2 is a system that combines large language models (LLMs) and reinforcement learning (RL) to automatically optimize Half-precision General Matrix Multiply (HGEMM) CUDA kernels. CUDA-L2 ...

The Matrix View in Cakewalk is a powerful tool for organizing and triggering musical ideas in a non-linear fashion, ideal for live performances, composing, or experimenting with loops and clips. This ...

Dozens of machine learning algorithms require computing the inverse of a matrix. Computing a matrix inverse is conceptually easy, but implementation is one of the most challenging tasks in numerical ...

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of computing a matrix inverse using the Newton iteration algorithm. Compared to other algorithms, Newton ...

Matrix factorization techniques, such as principal component analysis (PCA) and independent component analysis (ICA), are widely used to extract geological processes from geochemical data. However, ...

Rapid and Non-Targeted Qualitative and Quantitative Detection of miRNA in Complex Biological Samples Using Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry with a 3‑Aminoquinoline and ...

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.

Abstract: Efficiently synthesizing an entire application that consists of multiple algorithms for hardware implementation is a very difficult and unsolved problem. One of the main challenges is the ...