Large sparse linear systems arise in diverse fields such as structural engineering, fluid dynamics, network analysis and machine learning. Direct factorisation techniques often become impractical for ...

As Transformer models continue to grow in size and complexity, numerous high-fidelity pruning methods have been proposed to mitigate the increasing parameter count. However, transforming these ...

Abstract: Sparse Matrix-Matrix Multiplication (SpMM) is a fundamental operation in graph computing and analytics. However, the irregularity of real-world graphs poses significant challenges to ...

Abstract: Contemporary GPU architectures integrate specialized computing units for matrix multiplication, named matrix multiplication units (MXUs), to effectively process neural network applications.

Compare complexity of simple and fast transpose using counter.