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 ...
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 ...
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.
A new technical paper titled “Scalable MatMul-free Language Modeling” was published by UC Santa Cruz, Soochow University, UC Davis, and LuxiTech. “Matrix multiplication (MatMul) typically dominates ...
Presenting an algorithm that solves linear systems with sparse coefficient matrices asymptotically faster than matrix multiplication for any ω > 2. Our algorithm can be viewed as an efficient, ...
I'm trying to restrict the problem, but for now it seems that with newer numpy versions on x64 certain complex products return different results depending on whether the operands are wrapped in a ...
Matrix Multiplication-Free Language Models Maintain Top-Tier Performance at Billion-Parameter Scales
Matrix multiplication (MatMul) is a fundamental operation in most neural networks, primarily because GPUs are highly optimized for these computations. Despite its critical role in deep learning, ...
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