In this video from PASC17, Alfio Lazzaro (University of Zurich, Switzerland) presents: Increasing Efficiency of Sparse Matrix-Matrix Multiplication. “Matrix-matrix multiplication is a basic operation ...

Sparse matrix computations are prevalent in many scientific and technical applications. In many simulation applications, the solving of the sparse matrix-vector multiplication (SpMV) is critical for ...

Sparse matrix computations are pivotal to advancing high-performance scientific applications, particularly as modern numerical simulations and data analyses demand efficient management of large, ...

Abstract: Sparse-Dense Matrix Multiplication (SpMM) plays a key role in fields like machine learning, scientific simulations, and graph analytics. However, compared to Sparse Matrix-Vector ...

N:M sparsity is becoming increasingly popular for its potential to deliver high model accuracy and computational efficiency for deep learning. However, the real-world benefit of N:M sparsity is ...

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 ...