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MH-DCNet: An improved flow field prediction framework coupling neural network with physics solver

COMPUTERS & FLUIDS 2024年 284卷

作者： Xiao, Qisong Chen, Xinhai Liu, Jie Gong, Chunye Sun, Yufei Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha 410073 Peoples R China Natl Univ Def Technol Lab Digitizing Software Frontier Equipment Changsha 410073 Peoples R China Nankai Univ Coll Software Tianjin 300350 Peoples R China

With the development of intelligent computing technology, deep learning methods have provided an efficient solution for rapid flow field prediction in computational fluid dynamics (CFD) problems. However, existing methods have limitations in handling interference among physical variables due to different data distributions, leading to a decline in prediction performance. In this paper, we propose MH-DCNet, an improved flow field prediction framework that couples a neural network with a physics solver. Specifically, to address the data distribution problem, we design a multi-head deep convolutional neural network that decouples the prediction of physical variables through multiple encoders and decoders. We also develop a hybrid loss function by introducing the mean structural similarity to better capture the complex spatial structures and distribution features of flow fields. We evaluate MH-DCNet with unseen geometries and various flow conditions. Experimental results show that MH-DCNet outperforms other advanced models in efficiency and generalization capability. It accelerates the prediction process by 2.35 times compared to the CFD method while meeting the convergence constraints.

关键词： Computational fluid dynamics Deep learning Flow field prediction Hybrid loss function

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SCGraph: Accelerating Sample-based GNN Training by Staged Caching of Features on GPUs 20

SCGraph: Accelerating Sample-based GNN Training by Staged Ca...

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20th IEEE Int Symposium on parallel and distributed processing with Applicat / 15th IEEE Int Conf on Social Comp and Networking / 12th IEEE Int Conf on Big Data and Cloud Comp / 12th IEEE Int Conf on Sustainable Comp and Commun

作者： He, Yuqi Lai, Zhiquan Ran, Zhejiang Zhang, Lizhi Li, Dongsheng Natl Univ Def Technol Coll Comp Natl Key Lab Parallel & Distributed Proc Changsha Peoples R China

ISBN: (纸本)9781665464970

Graph neural networks (GNNs) have been becoming important tools for processing structured graph data and successfully applied to multiple graph-based application scenarios. The existing GNN systems adopt sample-based training on large-scale graphs over multiple GPUs. Although they support large-scale graph training, large data loading overhead of transferring vertex features between CPUs and GPUs is still a bottleneck. In this work, we propose SCGraph, a method that supports GPU high-speed feature caching. SCGraph classifies the graph vertices sorted by out-degrees. For high out-degree vertices, SCGraph sets grading caches via different GPUs to increase the overall cache content through NVLink high-speed data transmission between them. For low out-degree vertices, SCGraph expands training vertices' neighborhood in advance to regenerate cache. We evaluate SCGraph against two state-of-the-art industrial GNN frameworks, i.e., DGL and PaGraph on various benchmarks. Experimental results show that SCGraph improves the cache hit rate over GPUs up to 23.6%, and achieves up to 1.71x performance speedup over the state-of-the-art baselines while the convergence almost constant.

关键词： graph neural networks feature caching sampling

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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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PAGroup: Privacy-aware grouping framework for high-performance federated learning

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JOURNAL OF parallel AND distributed COMPUTING 2023年第1期175卷 37-50页

作者： Chang, Tao Li, Li Wu, MeiHan Yu, Wei Wang, Xiaodong Xu, ChengZhong Natl Univ Def Technol Coll Comp Key Lab Parallel & Distributed Comp Changsha Peoples R China Univ Macau State Key Lab Internet Things Smart City Zhuhai Peoples R China

Federated Learning is designed for multiple mobile devices to collaboratively train an artificial intelligence model while preserving data privacy. Instead of collecting the raw training data from mobile devices to the cloud, Federated Learning coordinates a group of devices to train a shared model in a distributed manner with the training data located on the devices. However, unbalanced data distribution and heterogeneous hardware configurations across different devices badly hurts the performance of collaborative model and severely impacts the overall training progress. Thus, a framework that can well balance the model accuracy and the training progress is urgently *** this paper, we propose PAGroup, a privacy-aware grouping framework for high-performance Federated Learning. PAGroup intelligently divides the participating clients into different groups through carefully analyzing the privacy requirement of the training data and the solid social relationship of the participating clients. After that, PAGroup conducts data shaping and capability-ware training in order to improve the model performance while accelerating the overall training process. We evaluate the performance of PAGroup with both simulation and hardware testbed. The evaluation results show that PAGroup improves model accuracy up to 21%. Meanwhile, it decreases 81% communication overhead, 29% computation cost and 84% wall-clock time at best comparing with the baselines.(c) 2023 Elsevier Inc. All rights reserved.

关键词： Federated learning Social relationship Client grouping

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Multi-level Contrastive Learning for Commonsense Question Answering 16th

Multi-level Contrastive Learning for Commonsense Question An...

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16th International Conference on Knowledge Science, Engineering and Management (KSEM)

作者： Fang, Quntian Huang, Zhen Zhang, Ziwen Hu, Minghao Hu, Biao Wang, Ankun Li, Dongsheng Natl Univ Def Technol Natl Key Lab Parallel & Distributed Proc Changsha Peoples R China Informat Res Ctr Mil Sci Beijing Peoples R China

ISBN: (纸本)9783031402913;9783031402920

Recent studies have shown that the integration of external knowledge greatly improves the performance of commonsense question answering. However, the problems of semantic representation discrepancy between questions and external knowledge as well as weak discrimination between choices have not been well ameliorated. To address the above problems, we propose Multi-Level Contrastive Learning named MLCL for commonsense question answering, which includes instance-level and class-level contrastive learning modules. The instance-level contrastive module aims to align questions with knowledge of correct choice in semantic space, and class-level contrastive module focuses on how to make it easier to distinguish between correct and wrong choices. The model achieves state-of-the-art result in CommonsenseQA dataset and outperforms competitive approaches in OpenBookQA. In addition, adequate experiments verify the effectiveness of contrastive learning in multi-choice commonsense question answering.

关键词： Contrastive learning Commonsense question answering External knowledge

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Bi-Objective Scheduling Algorithm for Hybrid Workflow in JointCloud 15

Bi-Objective Scheduling Algorithm for Hybrid Workflow in Joi...

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15th IEEE International Conference on Joint Cloud Computing (JCC)

作者： Li, Rui Wang, Huaimin Shi, Peichang Natl Univ Def Technol Coll Comp Sci Natl Key Lab Parallel & Distributed Proc Changsha 410073 Peoples R China Natl Univ Def Technol Coll Comp Sci State Key Lab Complex & Crit Software Environm Changsha 410073 Peoples R China

ISBN: (纸本)9798350387339

Big data workflows are widely used in IoT, recommended systems, and real-time vision applications, and they continue to grow in complexity. These hybrid workflows consist of both resource-intensive batch jobs and latency-sensitive stream jobs. Examples include the data analytics workflow, which incorporates batch data transformations and low-latency querying, and the machine learning workflow, which processes stream data feature extraction before performing batch training and low-latency inference. However, existing research on workflow scheduling primarily focuses on either stream or batch workflows, neglecting the efficient scheduling of hybrid workflows that respect their diverse resource requirements and the costly data transfers between them. In this article, we propose a hybrid workflow model that defines the optimal placement of hybrid workflows (OHWP) as a bi-objective optimization problem. Our proposed model takes into account parameters related to inter-communication between stream and batch jobs, as well as the heterogeneous resources in JointCloud environment. Additionally, we present OHWP-PS (OHWP on a Pruned Space), a scheduling algorithm for hybrid workflows that minimizes both cost and latency by improving the initial population and dynamically updating the search space. The results demonstrate that the proposed OHWP-PS algorithm is effective and competitive across all experiments.

关键词： batch and stream workflow scheduling hybrid workflow JointCloud bi-object optimization

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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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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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Accelerating Static Null Pointer Dereference Detection with parallel Computing 15

Accelerating Static Null Pointer Dereference Detection with ...

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15th Asia-Pacific Symposium on Internetware (Internetware)

作者： Xu, Rulin Chen, Luohui Zhang, Ruyi Zhang, Yuanliang Xiao, Wei Zhou, Haifang Mao, Xiaoguang Natl Univ Def Technol Coll Comp Changsha Peoples R China Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha Peoples R China

ISBN: (纸本)9798400707056

High-precision static analysis can effectively detect Null Pointer Dereference (NPD) vulnerabilities in C language, but the performance overhead is significant. In recent years, researchers have attempted to enhance the efficiency of static analysis by leveraging multicore resources. However, due to complex dependencies in the analysis process, the parallelization of static value-flow NPD analysis for large-scale software still faces significant challenges. It is difficult to achieve a good balance between detection efficiency and accuracy, which impacts its *** paper presents PANDA, the first parallel detector for high-precision static value-flow NPD analyzer in the C language. The core idea of PANDA is to utilize dependency analysis to ensure high precision while decoupling the strong dependencies between static value-flow analysis steps. This transforms the traditionally challenging-to-parallelize NPD analysis into two parallelizable algorithms: function summarization and combined query-based vulnerability analysis. PANDA introduces a task-level parallel framework and enhances it with a dynamic scheduling method to parallel schedule the above two key steps, significantly improving the performance and scalability of memory vulnerability *** implemented within the LLVM framework (version 15.0.7), PANDA demonstrates a significant advantage in balancing accuracy and efficiency compared to current popular open-source detection tools. In precision-targeted benchmark tests, PANDA maintains a false positive rate within 3.17% and a false negative rate within 5.16%;in historical CVE detection rate tests, its recall rate far exceeds that of comparative open-source tools. In performance evaluations, compared to its serial version, PANDA achieves up to an 11.23-fold speedup on a 16-node server, exhibiting outstanding scalability.

关键词： Static Analysis parallel Computing Value Flow Analysis NPD Detection

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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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