EDBT 2026 Demo / reviewers in the wild / expert
Julian Hammer
dblp:168/8227
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1
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% Embedded and real-time systems · 33% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computational science and engineering · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Embedded and real-time systems › model-based design
code generation |
0.4 | 1 | 2019 | Code generation for massively parallel phase-field simulations · SC 2019 |
High-performance computing › scientific computing systems
phase field simulation |
0.4 | 1 | 2019 | Code generation for massively parallel phase-field simulations · SC 2019 |
High-performance computing
scientific computing |
0.4 | 1 | 2019 | Code generation for massively parallel phase-field simulations · SC 2019 |
Computational science and engineering › materials science
materials science simulation |
0.1 | 1 | 2019 | Code generation for massively parallel phase-field simulations · SC 2019 |
Methods — techniques the papers use, named apart from their topics
stencil-based time-stepping · 0.8performance-portable code generation · 0.8free-energy functional transformation · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | Code generation for massively parallel phase-field simulationsabstractThis article describes the development of automatic program generation technology to create scalable phase-field methods for material science applications. To simulate the formation of microstructures in metal alloys, we employ an advanced, thermodynamically consistent phase-field method. A state-of-the-art large-scale implementation of this model requires extensive, time-consuming, manual code optimization to achieve unprecedented fine mesh resolution. Our new approach starts with an abstract description based on free-energy functionals which is formally transformed into a continuous PDE and discretized automatically to obtain a stencil-based time-stepping scheme. Subsequently, an automatized performance engineering process generates highly optimized, performance-portable code for CPUs and GPUs. We demonstrate the efficiency for real-world simulations on large-scale GPU-based (PizDaint) and CPU-based (SuperMUC-NG) supercomputers. Our technique simplifies program development and optimization for a wide class of models. Martin Bauer 0003, Johannes Hötzer, Dominik Ernst, Julian Hammer, Marco Seiz, Henrik Hierl, Jan Hönig, Harald Köstler, Gerhard Wellein, Britta Nestler, Ulrich Rüde |
SC | 4 |