I'm getting ready to start working on a C/C++ project that will be building and solving large tri-diagonal, block tri-diagonal and triangular matrices. I know there are a lot of libraries available ...

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

“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 ...

We present a new method for estimating multivariate, second-order stationary Gaussian Random Field (GRF) models based on the Sparse Precision matrix Selection (SPS) algorithm, proposed by Davanloo ...

DeepSeek updated an experimental AI model in what it called a step toward next-generation artificial intelligence.

Today NEC Corporation announced that it has developed Aurora Vector Engine data processing technology that accelerates the execution of machine learning on vector computers by more than 50 times in ...

This is a preview. Log in through your library . Abstract We consider the problem of fitting a generalized linear model to overdispersed data, focussing on a quasilikelihood approach in which the ...

It seems more vendors are looking beyond the x86 architecture for the big leaps in performance needed to power things like artificial intelligence (AI) and machine learning. Google and IBM have their ...