This work describes the development of matrix-free GPU-accelerated solvers for high-order
finite element problems in. The solvers are applicable to grad-div and Darcy problems in …

Scientific applications require solvers that work on many small size problems that are
independent from each other. At the same time, the high-end hardware evolves rapidly and …

This article describes a standard API for a set of Batched Basic Linear Algebra Subprograms
(Batched BLAS or BBLAS). The focus is on many independent BLAS operations on small …

As modern hardware keeps evolving, an increasingly effective approach to develo**
energy efficient and high-performance solvers is to design them to work on many small size …

Gaussian Elimination is commonly used to solve dense linear systems in scientific models.
In a large number of applications, a need arises to solve many small size problems, instead …

Many problems in engineering and scientific computing require the solution of a large
number of small systems of linear equations. Due to their high processing power, Graphics …

We present a high-performance GPU kernel with a substantial speedup over vendor
libraries for very small matrix computations. In addition, we discuss most of the challenges …

This paper presents a software framework for solving large numbers of relatively small
matrix problems using GPUs. Our approach combines novel and existing HPC techniques to …

This paper presents a GPU-accelerated Cholesky factorization for two different modes of
operation. The first one is the batch mode, where many independent factorizations on small …

Many crucial scientific computing applications, ranging from national security to medical
advances, rely on high-performance linear algebra algorithms and technologies …