Demo Data from EDBT 2026 / reviewers in the wild / expert
Timofey Mukha
dblp:223/9886
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2023
0000-0002-2195-8408ORCID · reported
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 · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
High-performance computing
scientific computing |
0.7 | 1 | 2023 | Exploring the Ultimate Regime of Turbulent Rayleigh-Bénard Convection Through Unprecedented Spectral-Element Simulations · SC 2023 |
High-performance computing › scientific computing systems › computational fluid dynamics
turbulence simulation |
0.7 | 1 | 2023 | Exploring the Ultimate Regime of Turbulent Rayleigh-Bénard Convection Through Unprecedented Spectral-Element Simulations · SC 2023 |
Methods — techniques the papers use, named apart from their topics
spectral-element method · 0.7in-situ data compression · 0.7GPU-optimized preconditioner · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Exploring the Ultimate Regime of Turbulent Rayleigh-Bénard Convection Through Unprecedented Spectral-Element SimulationsabstractWe detail our developments in the high-fidelity spectral-element code Neko that are essential for unprecedented large-scale direct numerical simulations of fully developed turbulence. Major innovations are modular multi-backend design enabling performance portability across a wide range of GPUs and CPUs, a GPU-optimized preconditioner with task overlapping for the pressure-Poisson equation and in-situ data compression. We carry out initial runs of Rayleigh-Bénard Convection (RBC) at extreme scale on the LUMI and Leonardo supercomputers. We show how Neko is able to strongly scale to 16,384 GPUs and obtain results that are not possible without careful consideration and optimization of the entire simulation workflow. These developments in Neko will help resolving the long-standing question regarding the ultimate regime in RBC. Niclas Jansson, Martin Karp, Adalberto Perez, Timofey Mukha, Yi Ju, Jiahui Liu 0006, Szilárd Páll, Erwin Laure, Tino Weinkauf, Jörg Schumacher, Philipp Schlatter, Stefano Markidis |
SC | 4 |