Demo Data from VLDB 2026 / reviewers in the wild / expert
Michael A. Sprague
dblp:284/2102
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2021
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
High-performance computing · 67% GPUs and heterogeneous computing · 33% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
High-performance computing › scientific computing systems
computational fluid dynamics |
0.5 | 1 | 2021 | Preparing an incompressible-flow fluid dynamics code for exascale-class wind energy simulations · SC 2021 |
GPUs and heterogeneous computing › GPU-accelerated scientific computing
GPU-accelerated simulation |
0.5 | 1 | 2021 | Preparing an incompressible-flow fluid dynamics code for exascale-class wind energy simulations · SC 2021 |
High-performance computing
scientific computing systems |
0.5 | 1 | 2021 | Preparing an incompressible-flow fluid dynamics code for exascale-class wind energy simulations · SC 2021 |
Methods — techniques the papers use, named apart from their topics
unstructured mesh · 0.5algebraic multigrid · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | Preparing an incompressible-flow fluid dynamics code for exascale-class wind energy simulationsabstractThe U.S. Department of Energy has identified exascale-class wind farm simulation as critical to wind energy scientific discovery. A primary objective of the ExaWind project is to build high-performance, predictive computational fluid dynamics (CFD) tools that satisfy these modeling needs. GPU accelerators will serve as the computational thoroughbreds of next-generation, exascale-class supercomputers. Here, we report on our efforts in preparing the ExaWind unstructured mesh solver, Nalu-Wind, for exascale-class machines. For computing at this scale, a simple port of the incompressible-flow algorithms to GPUs is insufficient. To achieve high performance, one needs novel algorithms that are application aware, memory efficient, and optimized for the latest-generation GPU devices. The result of our efforts are unstructured-mesh simulations of wind turbines that can effectively leverage thousands of GPUs. In particular, we demonstrate a first-of-its-kind, incompressible-flow simulation using Algebraic Multigrid solvers that strong scales to more than 4000 GPUs on the Summit supercomputer. Paul Mullowney, Stephen J. Thomas, Shreyas Ananthan, Ashesh Sharma, Jon S. Rood, Alan B. Williams, Michael A. Sprague |
SC | 8 |