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

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 ...

Aug. 6, 2024 — Lehigh University and Lawrence Berkeley National Laboratory (Berkeley Lab) researchers have developed an accelerating sparse accumulation (ASA) architecture, specialized hardware that ...

We live in the age of big data, but most of that data is “sparse.” Imagine, for instance, a massive table that mapped all of Amazon’s customers against all of its products, with a “1” for each product ...

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 ...

A band Lanczos algorithm for the iterative computation of eigenvalues and eigenvectors of a large sparse symmetric matrix is described and tested on numerical ...

In the 2003 hit film The Matrix Reloaded, Neo, portrayed by Keanu Reeves, asks the character known as The Architect, “Why am I here?” The Architect answers: “Your life is the sum of a remainder of an ...

“Several manufacturers have already started to commercialize near-bank Processing-In-Memory (PIM) architectures. Near-bank PIM architectures place simple cores close to DRAM banks and can yield ...