Today's floating-point arithmetic landscape is broader than ever. While scientific computing
has traditionally used single precision and double precision floating-point arithmetics, half …

Tensor Core is a mixed-precision matrix–matrix multiplication unit on NVIDIA GPUs with a
theoretical peak performance of more than 300 TFlop/s on Ampere architectures. Tensor …

Sparse matrix-vector multiplication (SpMV) plays a key role in computational science and
engineering, graph processing, and machine learning applications. Much work on SpMV …

Matrix engines or units, in different forms and affinities, are becoming a reality in modern
processors; CPUs and otherwise. The current and dominant algorithmic approach to Deep …

We introduce a novel approach to exploit mixed precision arithmetic for low-rank
approximations. Our approach is based on the observation that singular vectors associated …

We explore the floating-point arithmetic implemented in the NVIDIA tensor cores, which are
hardware accelerators for mixed-precision matrix multiplication available on the Volta …

Scientific applications are increasingly adopting Artificial Intelligence (AI) techniques to
advance science. High-performance computing centers are evaluating emerging novel …

Mixed-precision computing becomes an inevitable trend for HPC and AI applications due to
the increasing using mixed-precision units such as NVIDIA Tensor Cores. Fast Fourier …

In multiword arithmetic, a matrix is represented as the unevaluated sum of two or more lower
precision matrices, and a matrix product is formed by multiplying the constituents in low …

Deep learning hardware achieves high throughput and low power consumption by reducing
computing precision and specializing in matrix multiplication. For machine learning …