We discuss a scheme for hierarchical matrices with adaptive cross approximation on
symmetric multiprocessing clusters. We propose a set of parallel algorithms that are …

This paper presents a heterogeneous CPU-GPU implementation for a sparse iterative
eigensolver–the Locally Optimal Block Preconditioned Conjugate Gradient (LOBPCG). For …

In this paper we unveil some performance and energy efficiency frontiers for sparse
computations on GPU-based supercomputers. We compare the resource efficiency of …

Accelerating iterative eigenvalue algorithms is often achieved by employing a spectral
shifting strategy. Unfortunately, improved shifting typically leads to a smaller eigenvalue for …

Spectral Embedding (SE) is a popular method for dimensionality reduction, applicable
across diverse domains. Nevertheless, its current implementations face three prominent …

In this paper we unveil some energy efficiency and performance frontiers for sparse
computations on GPU-based supercomputers. To do this, we consider state-of-the-art …

We consider extrapolation of the Arnoldi algorithm to accelerate computation of the
dominant eigenvalue/eigenvector pair. The basic algorithm uses sequences of Krylov …

Kurzfassung Diese Arbeit behandelt die Konstruktion und Analyse von skalierbaren
Vorkonditionierungsstrategien für das lineare Schrödinger-Eigenwertproblem mit …

We consider algorithms that operate on sequences of Krylov vectors generated by the
(inverse) power method that accelerate computation of the dominant eigenvalue. The …

Resumen (English summary below) Las H-Matrices nacen como una potente herramienta
numérica para abordar aplicaciones cuyos datos generan estructuras que se sitúan entre …