Abstract: Sparse-Sparse matrix multiplication (SpMSpM) is a critical computation in various fields such as computational science and graph analysis. It poses computational challenges for ...

Hand-tuned WebAssembly implementations for efficient execution of web-based sparse computations including Sparse Matrix-Vector Multiplication (SpMV), sparse triangular solve (SpTS) and other useful ...

Hand-tuned WebAssembly implementations for efficient execution of web-based sparse computations including Sparse Matrix-Vector Multiplication (SpMV), sparse triangular solve (SpTS) and other useful ...

Researchers claim to have developed a new way to run AI language models more efficiently by eliminating matrix multiplication from the process. This fundamentally redesigns neural network operations ...

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, ...

Computer scientists have discovered a new way to multiply large matrices faster than ever before by eliminating a previously unknown inefficiency, reports Quanta Magazine. This could eventually ...

Abstract: The rising popularity of deep learning algorithms demands special accelerators for matrix-matrix multiplication. Most of the matrix multipliers are designed based on the systolic array ...

Hefei National Laboratory for Physical Sciences at the Microscale, Department of Chemical Physics, and Synergetic Innovation Center of Quantum Information and Quantum Physics, University of Science ...