125 citations · 387 across the 10 of their papers we have counts for
11 papers
A Scalable Sharp-Interface Immersed Boundary Method for Moving Boundary Flow Simulation on Multi-GPU Systems
Sushrut Kumar, Joshua Romero, Massimiliano Fatica +2
Sharp-interface immersed boundary methods provide a versatile framework for simulating flows with complex moving boundaries, but their irregular data structures, dynamic sparsity,…
GPU-Accelerated Simulations of Moving Boundary Problems and Fluid-Structure Interaction at Extreme Scales
Sushrut Kumar, Joshua Romero, Jung-Hee Seo +2
Computational fluid dynamics and fluid-structure interaction simulations involving moving and deforming bodies is extremely hard. In this work, we present a graphical processing un…
A fast incompressible Navier-Stokes solver for non-uniform grids
Pedro Costa, Duarte Palancha, Joshua Romero +2
We present a scalable incompressible Navier--Stokes solver for three-dimensional Cartesian grids with non-uniform spacing. A direct tensor-product--Thomas method solves the constan…
A GPU-Accelerated Sharp Interface Immersed Boundary Solver for Large Scale Flow Simulations
Sushrut Kumar, Joshua Romero, Jung-Hee Seo +2
Immersed boundary methods (IBMs) facilitate the simulation of flows around stationary, moving, and deforming bodies on Cartesian grids. However, extending these simulations to the…
FSEI-GPU: GPU accelerated simulations of the fluid-structure-electrophysiology interaction in the left heart
Francesco Viola, Vamsi Spandan, Valentina Meschini +4
The reliability of cardiovascular computational models depends on the accurate solution of the hemodynamics, the realistic characterization of the hyperelastic and electric propert…
One-point statistics for turbulent pipe flow up to
Sergio Pirozzoli, Joshua Romero, Massimiliano Fatica +2
We study turbulent flows in a smooth straight pipe of circular cross--section up to using direct--numerical-simulation (DNS) of the Navier--Stokes equations. Th…
Highly-scalable, physics-informed GANs for learning solutions of stochastic PDEs
Liu Yang, Sean Treichler, Thorsten Kurth +8
Uncertainty quantification for forward and inverse problems is a central challenge across physical and biomedical disciplines. We address this challenge for the problem of modeling…
Exascale Deep Learning for Scientific Inverse Problems
Nouamane Laanait, Joshua Romero, Junqi Yin +6
We introduce novel communication strategies in synchronous distributed Deep Learning consisting of decentralized gradient reduction orchestration and computational graph-aware grou…
A Performance Study of the 2D Ising Model on GPUs
Joshua Romero, Mauro Bisson, Massimiliano Fatica +1
The simulation of the two-dimensional Ising model is used as a benchmark to show the computational capabilities of Graphic Processing Units (GPUs). The rich programming environment…
Exascale Deep Learning for Climate Analytics
Thorsten Kurth, Sean Treichler, Joshua Romero +9
We extract pixel-level masks of extreme weather patterns using variants of Tiramisu and DeepLabv3+ neural networks. We describe improvements to the software frameworks, input pipel…
AFiD-GPU: a versatile Navier-Stokes Solver for Wall-Bounded Turbulent Flows on GPU Clusters
Xiaojue Zhu, Everett Phillips, Vamsi Spandan +9
The AFiD code, an open source solver for the incompressible Navier-Stokes equations ({\color{blue}\burl{http://www.afid.eu}}), has been ported to GPU clusters to tackle large-scale…