collaborators

5 papers

cs.DC2019

Two-level Dynamic Load Balancing for High Performance Scientific Applications

Ali Mohammed, Aurelien Cavelan, Florina M. Ciorba +2

Scientific applications are often complex, irregular, and computationally-intensive. To accommodate the ever-increasing computational demands of scientific applications, high-perfo…

cs.DC2019

Finding Neighbors in a Forest: A b-tree for Smoothed Particle Hydrodynamics Simulations

Aurélien Cavelan, Rubén M. Cabezón, Jonas H. M. Korndorfer +1

Finding the exact close neighbors of each fluid element in mesh-free computational hydrodynamical methods, such as the Smoothed Particle Hydrodynamics (SPH), often becomes a main b…

cs.DC2019

Algorithm-Based Fault Tolerance for Parallel Stencil Computations

Aurélien Cavelan, Florina M. Ciorba

The increase in HPC systems size and complexity, together with increasing on-chip transistor density, power limitations, and number of components, render modern HPC systems subject…

physics.comp-ph2019

SPH-EXA: Enhancing the Scalability of SPH codes Via an Exascale-Ready SPH Mini-App

Danilo Guerrera, Aurélien Cavelan, Rubén M. Cabezón +6

Numerical simulations of fluids in astrophysics and computational fluid dynamics (CFD) are among the most computationally-demanding calculations, in terms of sustained floating-poi…

cs.DC2019

Detection of Silent Data Corruptions in Smoothed Particle Hydrodynamics Simulations

Aurélien Cavelan, Rubén M. Cabezón, Florina M. Ciorba

Silent data corruptions (SDCs) hinder the correctness of long-running scientific applications on large scale computing systems. Selective particle replication (SPR) is proposed her…