We present algorithmic and implementation details for the fully self-consistent finite-
temperature GW method in Gaussian Bloch orbitals for solids. Our implementation is based …

Efficient exploitation of exascale architectures requires rethinking of the numerical
algorithms used in many large-scale applications. These architectures favor algorithms that …

High-dimensional partial differential equations (PDEs) are ubiquitous in economics, science
and engineering. However, their numerical treatment poses formidable challenges since …

The general matrix-matrix multiplication (GEMM) is the most important numerical kernel in
dense linear algebra, and is the key component for obtaining high performance in most …

This article is devoted to graphics processing unit (GPU) kernel optimization and
performance analysis of three tensor-product operations arising in finite element methods …

In this paper we describe the research and development activities in the Center for Efficient
Exascale Discretization within the US Exascale Computing Project, targeting state-of-the-art …

Tensor network methods are a class of numerical tools and algorithms to study many-body
quantum systems in and out of equilibrium, based on tailored variational wave functions …

Matrix multiplication (GEMM) is the most important operation in dense linear algebra.
Because it is a computebound operation that is rich in data reuse, many applications from …

The use of the general dense matrix-matrix multiplication (GEMM) is fundamental for
obtaining high performance in many scientific computing applications. GEMMs for small …

The numerical approximation of partial differential equations (PDEs) poses formidable
challenges in high dimensions since classical grid-based methods suffer from the so-called …