VLDB 2026 Research / reviewers in the wild / expert
Ivan I. Oleynik
dblp:304/5915
· 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 · 77% GPUs and heterogeneous computing · 23% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computational science and engineering · 100% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computational science and engineering › materials science
materials science simulation |
0.5 | 1 | 2021 | Billion atom molecular dynamics simulations of carbon at extreme conditions and experimental time and length scales · SC 2021 |
GPUs and heterogeneous computing › GPU-accelerated scientific computing
GPU-accelerated simulation |
0.1 | 1 | 2021 | Billion atom molecular dynamics simulations of carbon at extreme conditions and experimental time and length scales · SC 2021 |
Methods — techniques the papers use, named apart from their topics
kokkos · 1.0CUDA · 1.0machine-learning interatomic potential · 0.5machine learning interatomic potential · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | Billion atom molecular dynamics simulations of carbon at extreme conditions and experimental time and length scalesabstractBillion atom molecular dynamics (MD) using quantum-accurate machine-learning Spectral Neighbor Analysis Potential (SNAP) observed long-sought high pressure BC8 phase of carbon at extreme pressure (12 Mbar) and temperature (5,000 K). 24-hour, 4650 node production simulation on OLCF Summit demonstrated an unprecedented scaling and unmatched real-world performance of SNAP MD while sampling 1 nanosecond of physical time. Efficient implementation of SNAP force kernel in LAMMPS using the Kokkos CUDA backend on NVIDIA GPUs combined with excellent strong scaling (better than 97% parallel efficiency) enabled a peak computing rate of 50.0 PFLOPs (24.9% of theoretical peak) for a 20 billion atom MD simulation on the full Summit machine (27,900 GPUs). The peak MD performance of 6.21 Matom-steps/node-s is 22.9 times greater than a previous record for quantum-accurate MD. Near perfect weak scaling of SNAP MD highlights its excellent potential to advance the frontier of quantum-accurate MD to trillion atom simulations on upcoming exascale platforms. Kien Nguyen-Cong, Jonathan T. Willman, Stan G. Moore, Anatoly B. Belonoshko, Rahulkumar Gayatri, Evan Weinberg, Mitchell A. Wood, Aidan P. Thompson, Ivan I. Oleynik |
SC | 9 |