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Billion atom molecu...
Billion atom molecular dynamics simulations of carbon at extreme conditions and experimental time and length scales
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Nguyen-Cong, K. (författare)
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Willman, J. T. (författare)
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Moore, S. G. (författare)
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- Belonoshko, Anatoly (författare)
- KTH,Kondenserade materiens teori
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Gayatri, R. (författare)
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Weinberg, E. (författare)
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Wood, M. A. (författare)
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Thompson, A. P. (författare)
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Oleynik, I. I. (författare)
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(creator_code:org_t)
- 2021-11-13
- 2021
- Engelska.
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Ingår i: SC '21: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. - New York, NY, USA : Association for Computing Machinery (ACM).
- Relaterad länk:
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https://urn.kb.se/re...
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visa fler...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Billion 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.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Programvaruteknik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Software Engineering (hsv//eng)
Nyckelord
- carbon
- extreme conditions
- machine-learning interatomic potentials
- molecular dynamics
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)