Sparse matrix-matrix multiplication (SpMM) is a crucial kernel in various applications, including sparse deep neural networks [1]–[6], graph analytics [7], triangle counting [8], and linear algebra ...
A team of researchers developed “parallel optical matrix-matrix multiplication” (POMMM), which could revolutionize tensor ...
Dr. James McCaffrey presents a complete end-to-end demonstration of linear regression with pseudo-inverse training implemented using JavaScript. Compared to other training techniques, such as ...
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 idea that we might be living inside a vast computer simulation, much like in The Matrix, has fascinated philosophers and scientists for years. But a new study from researchers at the University of ...
Last year, onlookers observed a startling site on China’s Qiantang River: waves forming a grid-like pattern. Dubbed the “matrix tide,” this complex wave pattern was caused by the river’s famed tidal ...
BUFFALO, N.Y. — Last year, onlookers observed a startling site on China’s Qiantang River: waves forming a grid-like pattern. Dubbed the “matrix tide,” this complex wave pattern was caused by the river ...
Abstract: Distributed computations, such as distributed matrix multiplication, can be vulnerable to significant security issues, notably Byzantine attacks. These attacks may target either worker nodes ...
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 ...
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