Enterprise AI Analysis
Multiscale Light-Matter Dynamics in Quantum Materials: From Electrons to Topological Superlattices
This paper presents a paradigm shift that solves the multiscale/multiphysics/heterogeneity challenge by harnessing hardware heterogeneity and low-precision arithmetic. The MLMD software, integrating DC-MESH and XS-NNQMD modules, achieved unprecedented performance for complex quantum material simulations.
Executive Impact & Core Metrics
Our groundbreaking MLMD software, leveraging DC-MESH and XS-NNQMD on Aurora's 60,000 GPUs, delivers a 152x speedup for quantum dynamics and an astonishing 3,780x speedup for neural-network molecular dynamics, achieving 1.87 EFLOP/s. This allows the first-ever simulation of light-induced topological switching, crucial for future topotronics and AI.
0 EFLOP/s
Peak Quantum Dynamics Performance
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ME-NAQMD vs. SOTA Speedup
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XS-NNQMD vs. SOTA Speedup
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QD Weak-Scaling Parallel Efficiency
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NNQMD Weak-Scaling Parallel Efficiency
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Divide-conquer-recombine (DCR) algorithms partition multiscale/multiphysics problems into spatial and physical subproblems, each solved by appropriate computational methods on best-matching hardware units before recombination. This hierarchical decomposition enables optimal resource utilization and efficiency.
Metamodel-space algebra (MSA) allows subproblems to reside in respective hardware units, minimizing inter-unit communications. It integrates multiple methods using arithmetic operations in a metamodel space, optimizing precision and communication requirements by abstracting physical and spatial scales.
The excited-state neural-network quantum molecular dynamics (XS-NNQMD) module, leveraging Allegro-Legato and Allegro-FM, provides first-principles accuracy at a fraction of computational cost for light-induced switching of topological structures, enabling device-scale simulations for 'topotronics'.
Key implementation innovations include OpenMP target for portability, data/loop reordering, hierarchical parallelization, GEMMification for nonlocal corrections, GPU-resident kernels, parameterized mixed-precision computation, and ahead-of-time compilation for exascale performance and portability.
1.87 EFLOP/s
Peak Performance on Aurora for 15.4M-electron Quantum Dynamics
Enterprise Process Flow: Multiscale Dynamics Simulation
Multiscale/Multiphysics Problem
→
DCR Spatial & Physical Decomposition
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LFD (GPU) & QXMD (CPU) Processing
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MSA for Minimal Data Transfer
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XS-NNQMD (AI-driven Prediction)
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Topological Superlattice Switching (Topotronics)
| Work | Benchmark System | Machine | Time-to-solution [sec] | PFLOP/s (% of FP64 peak) |
|---|---|---|---|---|
| Qb@ll (2016) [21] | Aluminum, 59,400 electrons | IBM BlueGene/Q | 8.96 × 10⁻⁴ | 8.75 (43.5) |
| PWDFT (2020) [22] | Silicon, 3,072 electrons | Summit | 8.49 × 10⁻⁴ | 0.12 (2.0) |
| SALMON (2022) [23] | Silica, 71,040 electrons | Fugaku | 1.69 × 10⁻⁵ | 2.69 (3.17) |
| This work | PbTiO₃, 15,360,000 electrons | Aurora | 1.11 × 10⁻⁷ | 1873 (100.2) |
| Work | Machine | Time-to-solution [sec] |
|---|---|---|
| Linker et al. (2022) [11] | Theta | 7.09 × 10⁻¹² |
| This work | Aurora | 1.88 × 10⁻¹⁵ |
Light-Induced Topological Switching for Ferroelectric Topotronics
This work enabled the first study of light-induced switching of topological superlattices for future ferroelectric 'topotronics'. By combining DC-MESH and XS-NNQMD, the simulation can model ultrafast laser-light pulses interacting with PbTiO₃ material to understand and control emergent polarization topologies, paving the way for ultralow-power, ultrafast AI devices. This marks a significant step towards quantum materials-based computing and sensing.
| Implementation | Target | Runtime (s) | Speedup |
|---|---|---|---|
| Baseline | CPU | 8.655 | 1 |
| Data & loop re-ordering (Sec. 5.2.2) | CPU | 2.356 | 3.67 |
| Blocking / tiling (Sec. 5.2.3) | CPU | 0.939 | 9.22 |
| Hierarchical parallel regions (Sec. 5.2.4) | GPU | 0.026 | 338 |
3,780x
Faster than SOTA for XS-NNQMD Simulations
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Potential Annual Savings
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Your AI Implementation Roadmap
A phased approach to integrate these cutting-edge AI methodologies into your enterprise workflows.
Phase 01: Strategic Assessment & Data Integration
Conduct a deep dive into existing infrastructure and identify optimal data sources for DCR and MSA. Establish secure data pipelines for quantum material datasets.
Phase 02: MLMD Software Deployment & Customization
Deploy MLMD with DC-MESH and XS-NNQMD modules on your HPC platform, leveraging parameterized mixed-precision and GPU-resident kernels for initial benchmarks.
Phase 03: Topological Superlattice Simulation & Optimization
Initiate simulations of light-induced topological switching. Optimize DCR and MSA parameters for your specific quantum materials, ensuring peak performance and accuracy.
Phase 04: Real-world Application & Scalability
Scale simulations to device-relevant dimensions. Integrate AI-enhanced insights into your R&D pipelines for advanced topotronics and quantum material discovery.
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