Exploiting the Performance Potential of Extreme-Scale Earthquake Simulation: Achieving 86.7 PFLOPS With Over 39 Million Cores

IF 5.9 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

IEEE Transactions on Parallel and Distributed Systems Pub Date : 2026-01-28 DOI:10.1109/TPDS.2026.3658568

Lin Gan;Wubing Wan;Zekun Yin;Wenqiang Wang;Yilong Li;Zhenguo Zhang;Zhong He;Ping Gao;Xiaohui Duan;Weiguo Liu;Wei Xue;Haohuan Fu;Guangwen Yang;Xiaofei Chen

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引用次数: 0

Abstract

Leveraging the latest Sunway supercomputer, we developed a fully optimized earthquake simulation model that accurately captures topographic effects for realistic seismic analysis. Optimizing for the SW26010Pro architecture with DMA/RMA communication mechanisms, data compression schemes, and vectorization, we achieved a speedup exceeding 160×. Our pipeline-based computation and communication overlapping scheme, combined with performance prediction models further minimized computational costs. These optimizations enabled the largest-scale curvilinear grid finite-difference method (CGFDM) earthquake simulations to date, covering 197 trillion grid points and achieving 86.7 PFLOPS on 39 million cores with a weak scaling efficiency of 97.9%. These advancements enabled the successful simulation of the 2008 Wenchuan earthquake, providing high-resolution seismic insights and robust assessments for regional hazard mitigation and disaster preparedness.

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挖掘极端尺度地震模拟的性能潜力：用超过3900万核实现86.7 PFLOPS

利用最新的神威超级计算机，我们开发了一个全面优化的地震模拟模型，准确捕捉地形效应，进行真实的地震分析。通过DMA/RMA通信机制、数据压缩方案和矢量化对SW26010Pro架构进行优化，我们实现了超过160倍的加速。我们基于管道的计算和通信重叠方案，结合性能预测模型，进一步降低了计算成本。这些优化实现了迄今为止最大规模的曲线网格有限差分法（CGFDM）地震模拟，覆盖了197万亿个网格点，在3900万个核上实现了86.7 PFLOPS，缩放效率为97.9%。这些进展使2008年汶川地震的成功模拟成为可能，为区域减灾和备灾提供了高分辨率的地震信息和可靠的评估。

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来源期刊

IEEE Transactions on Parallel and Distributed Systems 工程技术-工程：电子与电气

CiteScore

11.00

自引率

9.40%

发文量

281

审稿时长

5.6 months

期刊介绍： IEEE Transactions on Parallel and Distributed Systems (TPDS) is published monthly. It publishes a range of papers, comments on previously published papers, and survey articles that deal with the parallel and distributed systems research areas of current importance to our readers. Particular areas of interest include, but are not limited to: a) Parallel and distributed algorithms, focusing on topics such as: models of computation; numerical, combinatorial, and data-intensive parallel algorithms, scalability of algorithms and data structures for parallel and distributed systems, communication and synchronization protocols, network algorithms, scheduling, and load balancing. b) Applications of parallel and distributed computing, including computational and data-enabled science and engineering, big data applications, parallel crowd sourcing, large-scale social network analysis, management of big data, cloud and grid computing, scientific and biomedical applications, mobile computing, and cyber-physical systems. c) Parallel and distributed architectures, including architectures for instruction-level and thread-level parallelism; design, analysis, implementation, fault resilience and performance measurements of multiple-processor systems; multicore processors, heterogeneous many-core systems; petascale and exascale systems designs; novel big data architectures; special purpose architectures, including graphics processors, signal processors, network processors, media accelerators, and other special purpose processors and accelerators; impact of technology on architecture; network and interconnect architectures; parallel I/O and storage systems; architecture of the memory hierarchy; power-efficient and green computing architectures; dependable architectures; and performance modeling and evaluation. d) Parallel and distributed software, including parallel and multicore programming languages and compilers, runtime systems, operating systems, Internet computing and web services, resource management including green computing, middleware for grids, clouds, and data centers, libraries, performance modeling and evaluation, parallel programming paradigms, and programming environments and tools.