Enterprise AI Analysis
Deep Learning-Enabled Supercritical Flame Simulation
This paper presents significant optimizations for DeepFlame—a deep learning-enabled supercritical flame simulation software—addressing its computational bottlenecks and enabling unprecedented scale and efficiency on exascale supercomputers. We detail a two-level parallelism scheme, advanced DNN inference optimizations, and novel I/O strategies that collectively achieve a 10,000x speedup compared to conventional methods. This breakthrough facilitates high-fidelity simulations of rocket engine combustion at scales previously unattainable, establishing DeepFlame as a critical tool for next-generation propulsion systems.
Executive Impact
DeepFlame's advancements redefine the benchmarks for high-fidelity simulation, delivering unparalleled scale and performance critical for next-generation aerospace and energy applications.
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Peak Performance (Mixed FP16)
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Simulation Scale Achieved
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Faster vs. Conventional Methods
Deep Analysis & Enterprise Applications
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Parallel Computing
Computational Efficiency
I/O Performance
Our two-level parallelization scheme addresses the inability to utilize modern many-core supercomputers, enabling efficient computing on million-core architectures.
Enterprise Process Flow
Process-level Mesh Decomposition
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Thread-level Mesh Decomposition
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Sub-region for One Core
94.9%
Weak Scaling Efficiency (Structured Grid)
Optimizations for DNN inference and PDE solving modules maximize floating-point performance, particularly through a mesh decomposition-based PDE solver.
| Optimization Step | Sunway Speedup (x) | Fugaku Speedup (x) | Key Features |
|---|---|---|---|
| Baseline | 1.0x | 1.0x |
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| Mixed-precision | 1.4x | 1.3x |
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| Tabulation (GeLU) | 2.1x | 1.7x |
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| Architecture-specific | 4.2x | 3.3x |
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37.4%
Peak FP32 Efficiency on Fugaku
Three I/O optimization strategies overcome bottlenecks in ultra-large-scale unstructured mesh combustion simulations.
Addressing Large-Scale I/O Bottlenecks
Description: Large-scale combustion simulations, especially those involving unstructured meshes, are frequently hindered by I/O performance limitations. The DeepFlame project identified key bottlenecks in initial data generation, collated storage format limitations, and concurrent file access overhead.
Challenge: Simulations scaled to 589,824 processes result in terabyte-scale data files, causing significant overhead for reading/writing. The OpenFOAM's collated storage lacks parallel I/O support, leading to linear increases in I/O time. Simultaneous file access by many processes also creates high overhead.
Solution:
1. Runtime Mesh Refinement: Integrates mesh refinement with computation, eliminating the need to read/write TB-level files by only reading coarse meshes (121TB down to 16GB input).
2. Foam File Indexing: Pre-generates an index file for collated files, enabling parallel I/O by recording start/end positions for each process.
3. Grouped Parallel I/O: Partitions processes into groups, with the first process in each group reading and scattering data to reduce concurrent file access and communication volume.
Outcome: These optimizations resolved long-standing I/O issues, making 618 billion cell simulations possible. Input file size reduced from 121 TB to 16 GB. Parallel efficiency maintained across large process counts.
16 GB
Reduced Input File Size (from 121TB)
Advanced ROI Calculator
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Estimated Annual Savings
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Hours Reclaimed Annually
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Implementation Timeline
Our structured approach ensures a smooth and efficient integration of DeepFlame into your existing workflows, delivering rapid value.
Phase 1: Discovery & Assessment (1-2 Weeks)
Detailed analysis of current simulation workflows, infrastructure, and performance bottlenecks. Identification of key areas for DeepFlame integration and customization.
Phase 2: Customization & Integration (4-6 Weeks)
Tailoring DeepFlame models to your specific chemical mechanisms and real-fluid conditions. Integration with existing HPC environments and data pipelines.
Phase 3: Validation & Benchmarking (2-3 Weeks)
Rigorous testing and validation against your established benchmarks and experimental data to ensure accuracy and performance gains.
Phase 4: Deployment & Training (1-2 Weeks)
Full deployment of the optimized DeepFlame solution. Comprehensive training for your engineering and research teams to maximize adoption and utilization.
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