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Nariman Piroozan

dblp:360/2899 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2025
0009-0005-3065-8609ORCID · 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 · 56% Hardware accelerators and domain-specific architectures · 44%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Computational science and engineering · 100%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
High-performance computing › supercomputing
exascale computing
0.912025
Multiscale Light-Matter Dynamics in Quantum Materials: From Electrons to Topological Superlattices · SC 2025
Hardware accelerators and domain-specific architectures › machine learning accelerator
low-precision arithmetic
0.912025
Multiscale Light-Matter Dynamics in Quantum Materials: From Electrons to Topological Superlattices · SC 2025
High-performance computing
quantum mechanical simulation
0.312025
Multiscale Light-Matter Dynamics in Quantum Materials: From Electrons to Topological Superlattices · SC 2025

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

neural-network quantum molecular dynamics · 1.7metamodel-space algebra · 1.7divide-conquer-recombine · 1.7
YearPublicationVenuePosition
2025 Multiscale Light-Matter Dynamics in Quantum Materials: From Electrons to Topological Superlattices
abstract
Light-matter dynamics in topological quantum materials enables ultralow-power, ultrafast devices. A challenge is simulating multiple field and particle equations for light, electrons, and atoms over vast spatiotemporal scales on Exaflop/s computers with increased heterogeneity and low-precision focus. We present a paradigm shift that solves the multiscale/multiphysics/heterogeneity challenge harnessing hardware heterogeneity and low-precision arithmetic. Divide-conquer-recombine algorithms divide the problem into not only spatial but also physical subproblems of small dynamic ranges and minimal mutual information, which are mapped onto best-characteristics-matching hardware units, while metamodel-space algebra minimizes communication and precision requirements. Using 60,000 GPUs of Aurora, DC-MESH (divide-and-conquer Maxwell-Ehrenfest-surface hopping) and XS-NNQMD (excited-state neural-network quantum molecular dynamics) modules of MLMD (multiscale light-matter dynamics) software were 152- and 3,780-times faster than the state-of-the-art for 15.4 million-electron and 1.23 trillion-atom PbTiO3 material, achieving 1.87 EFLOP/s for the former. This enabled the first study of light-induced switching of topological superlattices for future ferroelectric ‘topotronics’.
Taufeq Mohammed Razakh, Thomas Linker, Ye Luo 0001, Nariman Piroozan, Simon J. Pennycook, Nalini Kumar, Albert Musaelian, Anders Johansson 0011, Boris Kozinsky, Rajiv K. Kalia, Priya Vashishta, Fuyuki Shimojo, Shinnosuke Hattori, Ken-ichi Nomura, Aiichiro Nakano
SC4