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

Chinese researchers have made a significant breakthrough in the field of computing by developing a high-precision scalable analog matrix computing chip. This new analog chip is touted to be 1,000 ...

DGIST announced on July 4 that Professor Min-Soo Kim's team in the Department of Information and Communication Engineering developed the DistME (Distributed Matrix Engine) technology that can analyze ...

Gates-backed silicon photonics startup unveils optical transistors 10,000× smaller and chips with 1,000× squared matrix compute potential.

Researchers have developed an easy-to-use optical chip that can configure itself to achieve various functions. The positive real-valued matrix computation they have achieved gives the chip the ...

Most traditional high-performance computing applications focus on computations on very large matrices. Think seismic analysis, weather prediction, structural analysis. But today, with advances in deep ...

Topics covered include: approximations in computing, computer arithmetic, interpolation, matrix computations, nonlinear equations, optimization, and initial-value problems with emphasis on the ...

In this video, Michael Garland discusses algorithmic design on GPUs with some emphasis on sparse matrix computation. Recorded at the 2010 Virtual Summer School of Computation Science and Engineering ...

Topics covered include: approximations in computing, computer arithmetic, interpolation, matrix computations, nonlinear equations, optimization, and initial-value problems with emphasis on the ...