SpMV: Sparse Matrix–Vector Multiplication, a core operation in many numerical algorithms where a sparse matrix is multiplied by a vector.

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 ...

This paper presents a numerical comparison between algorithms for unconstrained optimization that take account of sparsity in the second derivative matrix of the objective function. Some of the ...

Can artificial intelligence (AI) create its own algorithms to speed up matrix multiplication, one of machine learning’s most fundamental tasks? Today, in a paper published in Nature, DeepMind ...

The sparsity constrained rank-one matrix approximation problem is a difficult mathematical optimization problem which arises in a wide array of useful applications in engineering, machine learning, ...

A novel AI-acceleration paper presents a method to optimize sparse matrix multiplication for machine learning models, particularly focusing on structured sparsity. Structured sparsity involves a ...

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 ...

Numenta announced a technology demonstration showing their brain-derived sparse networks perform inference tasks 50x faster than dense networks.

A combined sparse matrix and incomplete factorial screens, samples pH 3.5 to 8.5, low ionic strength, high ionic strength and mixed polymer/salt conditions and halides for potential phasing (see ...