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

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 ...

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 ...

Matrix multiplication is at the heart of many scientific applications and has been optimized to run on both the host Intel Xeon CPUs as well as the Intel Xeon Phi coprocessors. Matrix multiplies can ...

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 ...

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 ...