We present a sparse linear system solver that is based on a multifrontal variant of Gaussian
elimination and exploits low-rank approximation of the resulting dense frontal matrices. We …

The emergence of accelerators as standard computing resources on supercomputers and
the subsequent architectural complexity increase revived the need for high-level parallel …

Supplement to “Prediction models for network-linked data”. We provide the proof of
theoretical properties, computational complexity, additional simulation examples under …

Climate and weather can be predicted statistically via geospatial Maximum Likelihood
Estimates (MLE), as an alternative to running large ensembles of forward models. The MLE …

To face the advent of multicore processors and the ever increasing complexity of hardware
architectures, programming models based on DAG parallelism regained popularity in the …

High performance fast multipole method is crucial for the numerical simulation of many
physical problems. In a previous study, we have shown that task‐based fast multipole …

We show how to exploit graph sparsity in the Floyd-Warshall algorithm for the all-pairs
shortest path (Apsp) problem. Floyd-Warshall is an attractive choice for Apsp on high …

This paper highlights the necessary development of new instrumentation tools within the
PaRSE task-based runtime system to leverage the performance of low-rank matrix …

Summary Programming paradigms in High‐Performance Computing have been shifting
toward task‐based models that are capable of adapting readily to heterogeneous and …

We present a general framework that couples the PaRSEC runtime system and the HiCMA
numerical library to solve challenging 3D data-sparse problems. Though formally dense …