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 ...

Analog computers are systems that perform computations by manipulating physical quantities such as electrical current, that map math variables, instead of representing information using abstraction ...

1 Department of Statistics and Mathematics, Bindura University of Science Education, Bindura, Zimbabwe 2 Department of Mathematics, University of Botswana, Gaborone, Botswana This study develops a ...

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 ...

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 ...

Abstract: Sparse matrix-matrix multiplication is a critical kernel for several scientific computing applications, especially the setup phase of algebraic multigrid. The MPI+X programming model, which ...

In the quest to transform organizations, leaders often champion bold visions: compelling declarations of a better future. Yet many of these dreams fizzle away. Why? Because they fail to bridge the ...

The Nature Index 2025 Research Leaders — previously known as Annual Tables — reveal the leading institutions and countries/territories in the natural and health sciences, according to their output in ...

A standard digital camera used in a car for stuff like emergency braking has a perceptual latency of a hair above 20 milliseconds. That’s just the time needed for a camera to transform the photons ...