Exascale GPU-accelerated fluid flow solver
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Published in ArXiv, 2022
This paper assesses and reports the experience of eleven application teams working to build, validate, and benchmark several HPC applications on a novel GPU-accelerated Arm testbed. The testbed consists of the latest, at time of writing, Arm Devkits from NVIDIA with server-class Arm CPUs and NVIDIA A100 GPUs. The applications and mini-apps are written using multiple parallel programming models, including C++, C, CUDA, Fortran, OpenACC, and OpenMP. Each application builds extensively on the other tools available in the programming environment, including scientific libraries, compilers, and other tooling. Our goal is to evaluate application readiness for the next generation of Arm and GPU-based HPC systems and determine the tooling readiness for future application developers. On both accounts, the reported case studies demonstrate that the diversity of software and tools available for GPU-accelerated Arm systems are prepared for production, even before NVIDIA deploys their next-generation such platform: Grace.
Presented at the 2022 Georgia Scientific Computing Symposium (GSCS 2022) at the Georgia Institute of Technology.
Towards exascale multiphase compressible flow simulation via scalable interface capturing-based solvers and GPU acceleration
Research Poster presentation at American Physical Society > Division of Fluid Dynamics (APS > DFD). Link to the poster.
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.