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 ...
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 ...
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 ...
Nothing’s original Glyph Interface was the perfect level of gimmick — it added a bit of flair to the back of its first few phones, but always felt like it had a purpose. I trusted it for everything ...
Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Implementations of matrix multiplication via diffusion and reactions, thus eliminating ...
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 ...
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 ...
Abstract: Compute Unified Device Architecture (CUDA) was developed as a GPU parallel programming platform and API, primarily designed for use with C/C++. Over the years, fundamental linear algebra ...
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