This work revisits existing algorithms for the QR factorization of rectangular matrices
composed of p× q tiles, where p≥ q. Within this framework, we study the critical paths and …

Publisher Summary This chapter presents a hybridization methodology for the development
of high-performance linear algebra software for graphics processing units (GPUs). The …

One of the major trends in the design of exascale architectures is the use of multicore nodes
enhanced with GPU accelerators. Exploiting all resources of a hybrid accelerators-based …

Federated learning (FL) has emerged as a privacy-aware alternative to centralized data
analysis, especially for biomedical analyses such as genome-wide association studies …

Federated learning (FL) is emerging as a privacy-aware alternative to classical cloud-based
machine learning. In FL, the sensitive data remains in data silos and only aggregated …

We propose to study the impact on the energy footprint of two advanced algorithmic
strategies in the context of high performance dense linear algebra libraries:(1) mixed …

The advent of multicore processors represents a disruptive event in the history of computer
science as conventional parallel programming paradigms are proving incapable of fully …

This paper describes a new QR factorization algorithm which is especially designed for
massively parallel platforms combining parallel distributed nodes, where a node is a multi …

The Bulk Synchronous Parallel programming model is showing performance limitations at
high processor counts. We propose over-decomposition of the domain, operated on as …

As tile linear algebra algorithms continue achieving high performance on shared-memory
multicore architectures, it is a challenging task to make them scalable on distributed-memory …