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Dmytro Bykov

dblp:192/0108 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2024
0000-0002-6668-4586ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 2 · 2 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
2 papers
High-performance computing · 94% GPUs and heterogeneous computing · 6%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Computational science and engineering · 100%
Software engineering, system software, and programming languages
1 paper
Compilers and program optimization · 100%

Topics — the 8 heaviest of 9, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computational science and engineering › computational chemistry
ab initio molecular dynamics
0.812024
Breaking the Million-Electron and 1 EFLOP/s Barriers: Biomolecular-Scale Ab Initio Molecular Dynamics Using MP2 Potentials · SC 2024
Computational science and engineering › computational chemistry › molecular simulation
molecular dynamics
0.812024
Breaking the Million-Electron and 1 EFLOP/s Barriers: Biomolecular-Scale Ab Initio Molecular Dynamics Using MP2 Potentials · SC 2024
High-performance computing › scientific computing systems
biomolecular simulation
0.812024
Breaking the Million-Electron and 1 EFLOP/s Barriers: Biomolecular-Scale Ab Initio Molecular Dynamics Using MP2 Potentials · SC 2024
High-performance computing
scientific computing systems
0.812024
Breaking the Million-Electron and 1 EFLOP/s Barriers: Biomolecular-Scale Ab Initio Molecular Dynamics Using MP2 Potentials · SC 2024
High-performance computing › supercomputing
exascale computing
0.712023
Experiences readying applications for Exascale · SC 2023
High-performance computing
performance optimization at scale
0.712023
Experiences readying applications for Exascale · SC 2023
High-performance computing
supercomputing
0.712023
Experiences readying applications for Exascale · SC 2023
GPUs and heterogeneous computing
GPU-accelerated scientific computing
0.212024
Breaking the Million-Electron and 1 EFLOP/s Barriers: Biomolecular-Scale Ab Initio Molecular Dynamics Using MP2 Potentials · SC 2024

Methods — techniques the papers use, named apart from their topics

resolution-of-identity approximation · 1.5molecular fragmentation · 1.5asynchronous time stepping · 1.5MP2 perturbation theory · 1.5performance tuning · 1.3early access system evaluation · 1.3
YearPublicationVenuePosition
2024 Breaking the Million-Electron and 1 EFLOP/s Barriers: Biomolecular-Scale Ab Initio Molecular Dynamics Using MP2 Potentials
abstract
The accurate simulation of complex biochemical phenomena has historically been hampered by the computational requirements of high-fidelity molecular-modeling techniques. Quantum mechanical methods, such as ab initio wave-function (WF) theory, deliver the desired accuracy, but have impractical scaling for modeling biosystems with thousands of atoms. Combining molecular fragmentation with MP2 perturbation theory, this study presents an innovative approach that enables biomolecular-scale ab initio molecular dynamics (AIMD) simulations at WF theory level. Leveraging the resolution-of-the-identity approximation for Hartree-Fock and MP2 gradients, our approach eliminates computationally intensive four-center integrals and their gradients, while achieving near-peak performance on modern GPU architectures. The introduction of asynchronous time steps minimizes time step latency, overlapping computational phases and effectively mitigating load imbalances. Utilizing up to $\mathbf{9, 4 0 0}$ nodes of Frontier and achieving $\mathbf{5 9 \%}$ (1006.7 PFLOP/s) of its double-precision floating-point peak, our method enables us to break the million-electron and $1 \mathrm{EFLOP} / \mathrm{s}$ barriers for AIMD simulations with quantum accuracy.
Ryan Stocks, Jorge L. Galvez Vallejo, Fiona C. Y. Yu, Calum Snowdon, Elise Palethorpe, Jakub Kurzak, Dmytro Bykov, Giuseppe M. J. Barca
SC7
2023 Experiences readying applications for Exascale
abstract
The advent of Exascale computing invites an assessment of existing best practices for developing application readiness on the world's largest supercomputers. This work details observations from the last four years in preparing scientific applications to run on the Oak Ridge Leadership Computing Facility's (OLCF) Frontier system. This paper addresses a range of topics in software including programmability, tuning, and portability considerations that are key to moving applications from existing systems to future installations. A set of representative workloads provides case studies for general system and software testing. We evaluate the use of early access systems for development across several generations of hardware. Finally, we discuss how best practices were identified and disseminated to the community through a wide range of activities including user-guides and trainings. We conclude with recommendations for ensuring application readiness on future leadership computing systems.
Nicholas Malaya, O. E. Bronson Messer, Joseph Glenski, Antigoni Georgiadou, Justin Lietz, Kalyana C. Gottiparthi, Marcus S. Day, Jackie Chen, Jon S. Rood, Lucas Esclapez, James B. White III, Gustav R. Jansen, Nicholas Curtis, Stephen Nichols, Jakub Kurzak, Noel Chalmers, Chip Freitag, Paul T. Bauman, Alessandro Fanfarillo, Reuben D. Budiardja, Thomas Papatheodore, Nicholas Frontiere, Damon McDougall, Matthew R. Norman, Sarat Sreepathi, Philip C. Roth, Dmytro Bykov, Noah Wolfe, Paul Mullowney, Markus Eisenbach 0002, Marc T. Henry de Frahan, Wayne Joubert
SC27