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3DMeshNet: A three-dimensional differential neural network for structured mesh generation

GRAPHICAL MODELS 2025年 139卷

作者： Peng, Jiaming Chen, Xinhai Liu, Jie Natl Univ Def Technol Lab Digitizing Software Frontier Equipment Changsha 410073 Peoples R China Natl Univ Def Technol Natl Key Lab Parallel & Distributed Comp Changsha 410073 Peoples R China

Mesh generation is a crucial step in numerical simulations, significantly impacting simulation accuracy and efficiency. However, generating meshes remains time-consuming and requires expensive computational resources. In this paper, we propose a novel method, 3DMeshNet, for three-dimensional structured mesh generation. The method embeds the meshing-related differential equations into the loss function of neural networks, formulating the meshing task as an unsupervised optimization problem. It takes geometric points as input to learn the potential mapping between parametric and computational spaces. After suitable offline training, 3DMeshNet can efficiently output a three-dimensional structured mesh with a user-defined number of quadrilateral/hexahedral cells through the feed-forward neural prediction. To enhance training stability and accelerate convergence, we integrate loss function reweighting through weight adjustments and gradient projection alongside applying finite difference methods to streamline derivative computations in the loss. Experiments on different cases show that 3DMeshNet is robust and fast. It outperforms neural network- based methods and yields superior meshes compared to traditional mesh partitioning methods. 3DMeshNet significantly reduces training times by up to 85% compared to other neural network-based approaches and lowers meshing overhead by 4 to 8 times relative to traditional meshing methods.

关键词： Mesh generation Three-dimensional structured mesh Differential equations Neural network

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An adaptive gradient correction method based on mesh skewness for finite volume fluid dynamics simulations

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PHYSICS OF FLUIDS 2025年第1期37卷

作者： Song, Min Li, Chao Guo, Xiaowei Liu, Jie Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Lab Digitizing Software Frontier Equipment Changsha 410073 Hunan Peoples R China

In unstructured mesh solvers based on the finite volume method, accurate gradient calculation is crucial for determining the accuracy of equations. However, most mainstream computational fluid dynamics (CFD) software offer only uncorrected or globally corrected gradient options, which do not take into account local geometric characteristics. This limitation hampers computational efficiency and accuracy, especially in complex geometries, leading to poor convergence or even divergence, thus constraining its practical application in real-world engineering problems. To address these issues, this paper presents an adaptive gradient correction algorithm based on mesh skewness. By analyzing the geometric characteristics of mesh cells and dynamically applying a correction value, the algorithm modifies the gradient calculation formula adaptively to reduce numerical errors and improve accuracy, while ensuring stability. The algorithm has been successfully implemented in a general-purpose CFD software and validated through complex engineering cases such as sub-channel analysis, demonstrating its practical engineering value. Results indicate that for turbulent flow through a pipe, the proposed adaptive correction algorithm achieves an improved accuracy, demonstrating high precision and superiority in capturing intricate details of turbulence. In more complex scenarios like sub-channel analysis, the adaptive correction algorithm yields an error of 2.54% compared to 4.03% for the uncorrected approach. Unlike global correction methods that may lead to computational divergence within a few iterations, the adaptive correction method consistently maintains stability, accelerates convergence, and improves accuracy, offering significant theoretical and practical value.

关键词： Mesh generation

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A survey and benchmark evaluation for neural-network-based lossless universal compressors toward multi-source data

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FRONTIERS OF COMPUTER SCIENCE 2025年第7期19卷 197360-197360页

作者： Sun, Hui Ma, Huidong Ling, Feng Xie, Haonan Sun, Yongxia Yi, Liping Yan, Meng Zhong, Cheng Liu, Xiaoguang Wang, Gang Nankai Univ Coll Comp Sci Nankai Baidu Joint Lab GTIISCParallel & Distributed Software Technol Lab Tianjin 300350 Peoples R China Nanyang Technol Univ Coll Comp & Data Sci Singapore 639798 Singapore Guangxi Univ Inst Artificial Intelligence Sch Elect Engn Nanning 53004 Peoples R China Guangxi Univ Guangxi Univ & Coll Sch Comp Key Lab Parallel Distributed & Intelligent Nanning 53004 Peoples R China

As various types of data grow explosively, large-scale data storage, backup, and transmission become challenging, which motivates many researchers to propose efficient universal compression algorithms for multi-source data. In recent years, due to the emergence of hardware acceleration devices such as GPUs, TPUs, DPUs, and FPGAs, the performance bottleneck of neural networks (NN) has been overcome, making NN-based compression algorithms increasingly practical and popular. However, the research survey for the NN-based universal lossless compressors has not been conducted yet, and there is also a lack of unified evaluation metrics. To address the above problems, in this paper, we present a holistic survey as well as benchmark evaluations. Specifically, i) we thoroughly investigate NN-based lossless universal compression algorithms toward multi-source data and classify them into 3 types: static pre-training, adaptive, and semi-adaptive. ii) We unify 19 evaluation metrics to comprehensively assess the compression effect, resource consumption, and model performance of compressors. iii) We conduct experiments more than 4600 CPU/GPU hours to evaluate 17 state-of-the-art compressors on 28 real-world datasets across data types of text, images, videos, audio, etc. iv) We also summarize the strengths and drawbacks of NN-based lossless data compressors and discuss promising research directions. We summarize the results as the NN-based Lossless Compressors Benchmark (NNLCB, See ***/NNLCB website), which will be updated and maintained continuously in the future.

关键词： lossless compression benchmark evaluation universal compressors neural networks deep learning

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Cross-scale hierarchical spatio-temporal transformer for video enhancement

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KNOWLEDGE-BASED SYSTEMS 2025年 309卷

作者： Jiang, Qin Wang, Qinglin Chi, Lihua Liu, Jie Natl Univ Def Technol Changsha Peoples R China Lab Digitizing Software Frontier Equipment Changsha Peoples R China Sci & Technol Parallel & Distributed Proc Lab Changsha Peoples R China Hunan GuoKe Computil Technol Co Ltd Changsha Peoples R China

The diversity and complexity of degradations in low-quality videos pose non-trivial challenges on video enhancement to reconstruct the high-quality counterparts. Prevailing sliding window based methods represent poor performance due to the limitation of window size. Recurrent networks take advantage of long-term modeling to aggregate more information, resulting insignificant performance improvements. However, most of them are trained on simple degraded data and can only tackle specific degradation. To break through the limitation, we propose a progressive alignment network, namely Cross-scale Hierarchical Spatio-Temporal Transformer (CHSTT), which leverages cross-scale tokenization to generate multi-scale visual tokens in the entire sequence to capture extensive long-range temporal dependencies. To enhance the spatial and temporal interactions, we introduce an innovative hierarchical Transformer, facilitating the computation of mutual multi-head attention across both spatial and temporal dimensions. Quantitative and qualitative assessments substantiate the superior performance of CHSTT compared to several state-of-the-art benchmarks across three distinct video enhancement tasks, including video super-resolution, video denoising, and video deblurring.

关键词： Video super-resolution Denoising Deblurring Transformer Temporal

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GreenB plus Tree: an energy-efficient B plus tree for MIMD architectures

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CCF TRANSACTIONS ON HIGH PERFORMANCE COMPUTING 2025年第1期7卷 72-84页

作者： Peng, Muchun Wang, Qinglin Liang, Yuechao Guo, Weihao Yang, Shun Liang, Yaling Shi, Yongzhen Cao, Ligang Liu, Jie Natl Universe Def Technol Lab Digitizing Software Frontier Equipment Yanwachi St Changsha 410073 Hunan Peoples R China Natl Universe Def Technol Natl Key Lab Parallel & Distributed Comp Yanwachi St Changsha 410073 Hunan Peoples R China

In the current data-intensive landscape, B+trees are crucial data structures utilized across various fields like databases and web indexing. With the rise of data explosion, the demand for high-performance real-time query processing in database systems has surged. For instance, Alibaba's PolarDB and AnalyticDB systems handle massive query volumes and real-time data processing, highlighting the need for efficient solutions. Traditional approaches leveraging GPUs for B+tree performance enhancement have yielded positive results but suffer from high energy consumption, making their widespread deployment in large data centers impractical. This paper introduces GreenB+Tree, an energy-efficient B+tree optimized for the PEZY-SC3s chip, known for its high energy efficiency and MIMD architecture, which mitigates common GPU memory and warp divergence issues. GreenB+Tree innovatively transforms the B+tree structure into two one-dimensional arrays, significantly reducing memory costs and computational overhead. It further incorporates query-agglomerated optimization (QAO) and the persistent data residency strategy (PDRS) to minimize global memory access and enhance cache efficiency. Experimental evaluations demonstrate that GreenB+Tree achieves a throughput of 62.6 Million Queries Per Second per Watt (MQPS/W), outperforming contemporary GPU-based solutions by approximately 4.5 times.

关键词： B plus tree MIMD Energy-efficient

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A physics-informed generative adversarial network for advancing solutions in ocean acoustics

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PHYSICS OF FLUIDS 2025年第3期37卷

作者： Xia, Rui Guo, Xiao-Wei Zhang, Huajian Li, Genglin Xiao, Jing Xiao, Qisong Song, Min Li, Chao Liu, Jie Natl Univ Def Technol Lab Digitizing Software Frontier Equipment Changsha 410073 Peoples R China Natl Univ Def Technol Coll Comp Sci & Technol Changsha 410073 Peoples R China Natl Univ Def Technol Natl Key Lab Parallel & Distributed Comp Changsha 410073 Peoples R China

Advancements in artificial intelligence, notably the groundbreaking efforts in deep learning exemplified by physics-informed neural networks, have opened up innovative pathways for addressing intricate ocean acoustic problems. However, conventional physics-informed neural networks are limited in solving high-frequency forward and inverse problems. This paper introduces a novel physics-informed generative adversarial network integrating a forward-solving network (generator) and an inverse parameter-estimating network (discriminator). The generator network incorporates convolutional neural networks with hard-constrained boundary conditions and optimized loss functions to effectively predict the solution governed by the time-domain wave equation. For inverse problems, a discriminator is introduced for parameter estimation to complete the generative adversarial network. Furthermore, customized optimization strategies and an adaptive weighting loss function are devised to boost the training performance further. The test results of both forward and reverse cases show the advantage of our model over existing methods in terms of accuracy. The result indicates its vast potential for applications in ocean acoustics engineering.

关键词： Discriminators

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Accelerated Selective Algebraic Multigrid Method for Fully-Coupled Incompressible Flow Solver

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INTERNATIONAL JOURNAL OF COMPUTATIONAL FLUID DYNAMICS 2025年

作者： Liang, Yuechao Guo, Xiao-Wei Zhang, Qingyang Li, Chao Liu, Jie Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha Peoples R China Natl Univ Def Technol Lab Digitizing Software Frontier Equipment Changsha Peoples R China Natl Univ Def Technol Coll Comp Sci & Technol Changsha Peoples R China

The fully coupled pressure-based algorithm is widely recognised for its superior convergence and robustness in solving incompressible flow problems. However, the increased scale of equations and the difficulty in solving linear systems have limited the widespread use of this algorithm in large-scale simulations. This paper presents an optimised block selective algebraic multigrid method significantly reducing computational complexity. Our approach employs a parallel modified independent set algorithm, allowing each process to perform matrix coarsening individually. Furthermore, an aggressive coarsening strategy is introduced to reduce complexity and enable the solution of larger-scale problems. Numerical experiments demonstrate that the solution time is shortened by 14% to 49% compared to the latest existing methods and outperforms the segregated algorithm. By addressing the computational challenges associated with the selective algebraic multigrid solver, this work unleashes the superior convergence properties of the fully coupled method.

关键词： Fully coupled method velocity-pressure coupling selective algebraic multigrid multigrid parallelisation incompressible flow simulation

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FE-PIRBN: Feature-enhanced physics-informed radial basis neural networks for solving high-frequency electromagnetic scattering problems

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JOURNAL OF COMPUTATIONAL PHYSICS 2025年 527卷

作者： Zhang, Huajian Li, Chao Xia, Rui Chen, Xinhai Xiao, Tiaojie Guo, Xiao-Wei Liu, Jie Natl Univ Def Technol Coll Comp Sci & Technol Changsha 410000 Hunan Peoples R China Natl Univ Def Technol Lab Digitizing Software Frontier Equipment Changsha 410000 Hunan Peoples R China Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha 410000 Hunan Peoples R China

The solution of electromagnetic scattering problems holds significant application prospects in fields such as target detection and electromagnetic metamaterial design. However, efficient prediction of electromagnetic fields under high-frequency and intricate geometric conditions remains one of the most challenging problems. In this study, we proposed a feature-enhanced physics-informed radial basis neural network (FE-PIRBN) for solving high-frequency electromagnetic scattering problems. As a physics-driven neural network model, FE-PIRBN incorporates a PIRBN core network to preserve the local approximating property during the electromagnetic wave propagation process. It combines multi-resolution hash encoding techniques to optimize the capture of high-frequency electromagnetic field features. Under data-free training, the FE-PIRBN enables the accurate prediction of high-frequency electromagnetic scattering at sub-wavelength scales. Based on the testing results of typical cases including the electromagnetic scattering from single and double cylinder, it is evident that FE-PIRBN achieves computational accuracy within a 1.40%-5.82% relative error compared to numerical simulation results and the reference solutions. In increasing frequency scenarios, FE-PIRBN shows excellent generalization capabilities, achieving significant accuracy improvements and more robust convergence in high- frequency electromagnetic scattering tasks, ensuring future reliability and accuracy in wider band applications. Of particular importance is that as the electromagnetic frequency increases, the computational time for numerical methods grows exponentially. In contrast, FE-PIRBN, as a neural network model, maintains a relatively stable parameter size, resulting in significantly faster computation. This presents a promising and potential solution for addressing large-scale electromagnetic problems in the field.

关键词： High-frequency electromagnetic scattering Physics-informed neural network Hash encoding Radial basis neural network

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A heterogeneous parallel algorithm for the Cartesian discrete ordinates for multizone heterogeneous system

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JOURNAL OF SUPERCOMPUTING 2025年第4期81卷 1-33页

作者： Li, Runhua Wang, Qinglin Liu, Jie Natl Univ Def Technol Coll Comp Sci & Technol Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Lab Digitizing Software Frontier Equipment Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Natl Key Lab Parallel & Distributed Comp Changsha 410073 Hunan Peoples R China

Advancements in computing technology have revolutionized the efficiency and cost-effectiveness of realistic reactor core simulations. The discrete ordinates (SN\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$S_N$$\end{document}) method is a widely adopted approach for numerically solving the Boltzmann transport equation (BTE), which describes neutron distribution in nuclear reactors. Recently, the MT-3000, a novel multizone heterogeneous architecture designed for high-performance computing, has been developed, offering a peak double-precision performance of 11.6 TFLOPS at 1.2 GHz. In this study, we propose an efficient four-level heterogeneous SN\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$S_N$$\end{document} parallel algorithm for structured hexahedral grids on the MT-3000 system. The algorithm incorporates a two-level KBA strategy with an optimized communication scheme among the MT-3000's acceleration cores and employs a software caching technique to reduce memory access latency. Numerical experiments reveal that our algorithm achieves 1.68 TFLOPS on a single MT-3000 chip, representing 14.5% of its peak performance. The heterogeneous parallel algorithm demonstrates strong scaling efficiency, consistently exceeding 50% across 4 to 256 MT-3000 systems. Furthermore, we develop a performance model that closely aligns with the experimental results.

关键词： Discrete ordinates KBA Neutron transport High-performance computing MT-3000

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An efficient heterogeneous parallel password recovery system on MT-3000

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JOURNAL OF SUPERCOMPUTING 2025年第1期81卷 1-28页

作者： Luo, Yongtao Liu, Jie Gong, Chunye Li, Tun Natl Univ Def Technol Lab Digitizing Software Frontier Equipment Changsha 410073 Peoples R China Natl Univ Def Technol Natl Key Lab Parallel & Distributed Comp Changsha 410073 Peoples R China Natl Univ Def Technol Comp Sch Changsha 410073 Peoples R China Natl Supercomp Ctr Tianjin Tianjin 300457 Peoples R China

Password-based recovery is a widely used method for regaining access to applications or services when passwords are lost or forgotten. It is commonly used in electronic forensics by law enforcement agencies, information acquisition in the commercial sector, and data recovery for individuals. However, as encryption algorithms and complex passwords become more prevalent for security purposes, traditional CPU-based and GPU-based password recovery systems are struggling to meet the time-sensitive requirements for deciphering, and there is an urgent need for a more efficient password recovery system. Therefore, this paper presents an efficient heterogeneous parallel password recovery system based on the MT-3000 multi-zone processor. According to the architectural features of MT-3000, this system adopts a heterogeneous multi-level parallelism strategy, including inter-acceleration cluster data parallelism through MPI, intra-acceleration core data parallelism through the hthreads APIs, and instruction-level parallelism through a very long instruction word manner. Additionally, this system utilizes a unified task allocation mechanism that assigns the initialization and comparison verification tasks to the CPU side, while the accelerator side executes the hash iteration. This approach ensures that the system achieves optimal performance while maintaining its efficiency. The experimental analysis and results confirm that the proposed system significantly improves the recovery efficiency compared to traditional CPU-based and GPU-based systems, and also has an advantage in deciphering speed compared to the most popular hybrid CPU-FPGA-based system. Furthermore, it offers superior scalability, allowing for expansion to more compute nodes, making it a good solution for large-scale password recovery needs.

关键词： MT-3000 Password recovery Heterogeneous computing SIMD

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