Here’s a quick library to write your GPU-based operators and execute them in your Nvidia, AMD, Intel or whatever, along with my new VisualDML tool to design your operators visually. This is a follow ...

MIT researchers have designed silicon structures that can perform calculations in an electronic device using excess heat instead of electricity. These tiny structures could someday enable more ...

Abstract: Matrix multiplication is one of the most important operations in both scientific computing and deep-learning applications. However, on regular processors such as CPUs and GPUs, the ...

NVIDIA releases detailed cuTile Python tutorial for Blackwell GPUs, demonstrating matrix multiplication achieving over 90% of cuBLAS performance with simplified code. NVIDIA has published a ...

Abstract: Contemporary GPU architectures integrate specialized computing units for matrix multiplication, named matrix multiplication units (MXUs), to effectively process neural network applications.

Multiplication in Python may seem simple at first—just use the * operator—but it actually covers far more than just numbers. You can use * to multiply integers and floats, repeat strings and lists, or ...

Physics and Python stuff. Most of the videos here are either adapted from class lectures or solving physics problems. I really like to use numerical calculations without all the fancy programming ...

Discovering faster algorithms for matrix multiplication remains a key pursuit in computer science and numerical linear algebra. Since the pioneering contributions of Strassen and Winograd in the late ...

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.