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FMCC-RT: a scalable and fine-grained all-reduce algorithm for large-scale SMP clusters

Science China(Information Sciences) 2025年第5期68卷 362-379页

作者： Jintao PENG Jie LIU Jianbin FANG Min XIE Yi DAI Zhiquan LAI Bo YANG Chunye GONG Xinjun MAO Guo MAO Jie REN School of Computer Science and Technology National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology National Supercomputer Center in Tianjin School of Computer Science Shaanxi Normal University

All-reduce is a widely used communication technique for distributed and parallel applications typically implemented using either a tree-based or ring-based scheme. Each of these approaches has its own limitations: tree-based schemes struggle with efficiently exchanging large messages, while ring-based solutions assume constant communication throughput,an unrealistic expectation in modern network communication infrastructures. We present FMCC-RT, an all-reduce approach that combines the advantages of tree-and ring-based implementations while mitigating their drawbacks. FMCC-RT dynamically switches between tree and ring-based implementations depending on the size of the message being processed. It utilizes an analytical model to assess the impact of message sizes on the achieved throughput, enabling the derivation of optimal work partitioning parameters. Furthermore, FMCC-RT is designed with an Open MPI-compatible API, requiring no modification to user code. We evaluated FMCC-RT through micro-benchmarks and real-world application tests. Experimental results show that FMCC-RT outperforms state-of-the-art tree-and ring-based methods, achieving speedups of up to 5.6×.

关键词： all-reduce collective communication MPI scalability

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Isolate Sets Based parallel Louvain Method for Community Detection

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Journal of computer Science & Technology 2023年第2期38卷 373-390页

作者：郄航窦勇黄震熊运生 Science and Technology on Parallel and Distributed Laboratory School of Computer National University of Defense TechnologyChangsha 410073China

Community detection is a vital task in many fields,such as social networks and financial analysis,to name a *** Louvain method,the main workhorse of community detection,is a popular heuristic *** apply it to large-scale graph networks,researchers have proposed several parallel Louvain methods(PLMs),which suffer from two challenges:the latency in the information synchronization,and the community *** tackle these two challenges,we propose an isolate sets based parallel Louvain method(IPLM)and a fusion IPLM with the hashtables based Louvain method(FIPLM),which are based on a novel graph partition *** graph partition algorithm divides the graph network into subgraphs called isolate sets,in which the vertices are relatively decoupled from *** first describe the concepts and properties of the isolate *** we propose an algorithm to divide the graph network into isolate sets,which enjoys the same computation complexity as the breadth-first ***,we propose IPLM,which can efficiently calculate and update vertices information in parallel without latency or community ***,we achieve further acceleration by FIPLM,which maintains a high quality of community detection with a faster speedup than *** two methods are for shared-memory architecture,and we implement our methods on an 8-core PC;the experiments show that IPLM achieves a maximum speedup of 4.62x and outputs higher modularity(maximum 4.76%)than the serial Louvain method on 14 of 18 ***,FIPLM achieves a maximum speedup of 7.26x.

关键词： parallel computing isolate set graph partition Louvain method community detection

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SIGNGD with Error Feedback Meets Lazily Aggregated Technique:Communication-Efficient Algorithms for distributed Learning

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Tsinghua Science and Technology 2022年第1期27卷 174-185页

作者： Xiaoge Deng Tao Sun Feng Liu Dongsheng Li National Laboratory for Parallel and Distributed Processing(PDL) College of ComputerNational University of Defense TechnologyChangsha 410073China

The proliferation of massive datasets has led to significant interests in distributed algorithms for solving large-scale machine learning ***,the communication overhead is a major bottleneck that hampers the scalability of distributed machine learning *** this paper,we design two communication-efficient algorithms for distributed learning *** first one is named EF-SIGNGD,in which we use the 1-bit(sign-based) gradient quantization method to save the communication ***,the error feedback technique,i.e.,incorporating the error made by the compression operator into the next step,is employed for the convergence *** second algorithm is called LE-SIGNGD,in which we introduce a well-designed lazy gradient aggregation rule to EF-SIGNGD that can detect the gradients with small changes and reuse the outdated ***-SIGNGD saves communication costs both in transmitted bits and communication ***,we show that LE-SIGNGD is convergent under some mild *** effectiveness of the two proposed algorithms is demonstrated through experiments on both real and synthetic data.

关键词： distributed learning communication-efficient algorithm convergence analysis

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CD-Sched: An Automated Scheduling Framework for Accelerating Neural Network Training on Shared Memory CPU-DSP Platforms

CD-Sched: An Automated Scheduling Framework for Accelerating...

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2023 International Conference on Power, Communication, Computing and Networking Technologies, PCCNT 2023

作者： Xiao, Yuanyuan Lai, Zhiquan Li, Dongsheng National Key Laboratory of Parallel and Distributed Processing Computer College National University of Defense Technology Changsha China

ISBN: (纸本)9781450399951

DSP holds significant potential for important applications in Deep Neural Networks. However, there is currently a lack of research focused on shared-memory CPU-DSP heterogeneous chips. This paper proposes CD-Sched, an automated scheduling framework that aims to address this gap. By predicting the latency of operators on both CPU and DSP, CD-Sched automatically schedules the computation of operators to the appropriate computing device. This scheduling optimization accelerates the computation of individual operators and ultimately improves the overall training time of neural networks. In end-to-end training tasks, CD-Sched can significantly reduce the overall training time, with an average reduction of approximately 10.77%. © 2023 ACM.

关键词： Deep neural networks

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YFLM: An Improved Levenberg-Marquardt Algorithm for Global Bundle Adjustment 41st

YFLM: An Improved Levenberg-Marquardt Algorithm for Global ...

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41st computer Graphics International Conference, CGI 2024

作者： Peng, Jiaxin Li, Tao Jiang, Qin Liu, Jie Wang, Ruibo Laboratory of Software Engineering for Complex Systems School of Computer Science National University of Defense Technology Hunan Changsha410073 China Parallel and Distributed Processing Laboratory School of Computer Science National University of Defense Technology Hunan Changsha410073 China

ISBN: (纸本)9783031820205

The conventional Levenberg-Marquardt (LM) algorithm is a state-of-the-art trust-region optimization method for solving bundle adjustment problems in the Structure-from-Motion community, which not only takes advantage of the fast convergence of the Gauss-Newton method, but also the stability of the gradient descent method when approaching optimal solutions. However, the damping ratio of LM is simply provided by trial-and-error, which causes slow convergence rate for large-scale problems. This paper proposes the Yamashita-Fukushima LM (YFLM) algorithm to reduce the time complexity for global bundle adjustment, where the damping factor is determined by Yamashita and Fukushima’s method. YFLM dynamically calculates a more reasonable and optimal damping ratio according to the newest reprojection error. The experimental results show that the YFLM algorithm outperforms the conventional LM algorithm for most public bundle adjustment datasets. Besides this, the convergence of the YFLM algorithm is also evaluated with different σ∈(0,2]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Damping

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Smoothing Point Adjustment-Based Evaluation of Time Series Anomaly Detection 48

Smoothing Point Adjustment-Based Evaluation of Time Series A...

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48th IEEE International Conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Liu, Mingyu Wang, Yijie Xu, Hongzuo Zhou, Xiaohui Li, Bin Wang, Yongjun National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory College of Computer Changsha China

ISBN: (纸本)9781728163277

Anomalies in time series appear consecutively, forming anomaly segments. Applying the classical point-based evaluation metrics to evaluate the detection performance of segments leads to considerable underestimation, so most related studies resort to point adjustment. This operation treats all points as true positives within a segment equally when only one individual point alarms, resulting in significant overestimation and creating an illusion of superior performance. This paper proposes smoothing point adjustment, a novel range-based evaluation protocol for time series anomaly detection. Our protocol reflects detection performance impartially by carefully considering the specific location and frequency of alarms in the raw results. It is achieved by smoothly determining the adjustment range and rewarding early detection via a ranging function and a rewarding function. Compared with other evaluation metrics, experiments on different datasets show that our protocol can yield a performance ranking of various methods more consistent with the desired situation. © 2023 IEEE.

关键词： Anomaly Detection Evaluation Protocol Point Adjustment Time Series

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AFMA-Track: Adaptive Fusion of Motion and Appearance for Robust Multi-object Tracking 27th

AFMA-Track: Adaptive Fusion of Motion and Appearance for ...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Liao, Wei Luo, Lei Zhang, Chunyuan College of Computer Science and Technology National University of Defence Technology Changsha China Science and Technology on Parallel and Distributed Processing Laboratory College of Computer Science and Technology National University of Defense Technology Changsha China

ISBN: (纸本)9783031784439

Motion and appearance cues play a crucial role in Multi-object Tracking (MOT) algorithms for associating objects across consecutive frames. While most MOT methods prioritize accurate motion modeling and distinctive appearance representations, the use of appearance and motion cues is often confined to simplistic association techniques. For instance, fixed weights are commonly employed to combine the intersection-over-union (IoU) matrix and appearance similarity matrix, yielding an association cost matrix. To harness the full potential of motion and appearance cues across diverse scenarios, we propose an innovative approach that dynamically balances motion and appearance cues based on scene and object information during the association process. Furthermore, we introduce a new mechanism for updating appearance representations, effectively mitigating noise introduced by occlusion. Our method demonstrates state-of-the-art performance on the MOT17 and MOT20 test sets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Object detection

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Area-NeRF: Area-based Neural Radiance Fields 2

Area-NeRF: Area-based Neural Radiance Fields

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2nd International Conference on Image processing, computer Vision and Machine Learning, ICICML 2023

作者： Ye, Zonxin Li, Wenyu Qiao, Peng Dou, Yong National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing School of Computer Changsha China

ISBN: (纸本)9798350331417

Neural Radiance Field (NeRF) has received widespread attention for its photo-realistic novel view synthesis quality. Current methods mainly represent the scene based on point sampling of ray casting, ignoring the influence of the observed area changing with distance. In addition, The current sampling strategies are all focused on the distribution of sampling points on the ray, without paying attention to the sampling of the ray. We found that the current ray sampling strategy for scenes with the camera moving forward severely reduces the convergence speed. In this work, we extend the point representation to area representation by using relative positional encoding, and propose a ray sampling strategy that is suitable for camera trajectory moving forward. We validated the effectiveness of our method on multiple public datasets. © 2023 IEEE.

关键词： NeRF neural radiance field neural rendering novel view synthesis

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Optimizing Yinyang K-Means Algorithm on ARMv8 Many-Core CPUs 22nd

Optimizing Yinyang K-Means Algorithm on ARMv8 Many-Core CPU...

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22nd International Conference on Algorithms and Architectures for parallel processing, ICA3PP 2022

作者： Zhou, Tianyang Wang, Qinglin Yin, Shangfei Hao, Ruochen Liu, Jie Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China School of Computer Science National University of Defense Technology Changsha410073 China

ISBN: (纸本)9783031226762

K-Means algorithm is one of the most common clustering algorithms widely applied in various data analysis applications. Yinyang K-Means algorithm is a popular enhanced K-Means algorithm that avoids most unnecessary calculations using triangle inequality. However, Yinyang K-Means algorithm is time-consuming when the problem size is large. Due to the influence of performance and energy-efficiency, ARM CPUs have appeared in high performance computing. Therefore, it is very interesting to accelerate Yinyang K-Means algorithm on ARM CPUs. In this paper, we propose an efficient parallel implementation of Yinyang K-Means algorithm on ARMv8 many-core CPUs by means of vectorization, NUMA affinity memory optimization and data layout optimization. The experiment on two ARMv8 many-core CPUs has shown that our implementation can achieve up to 5.6 times faster than the open-source multi-threaded one of Yinyang K-Means algorithm. To the best of our knowledge, this is the first work that studies the optimization of Yinyang K-Means algorithms on ARMv8 CPUs. © 2023, Springer Nature Switzerland AG.

关键词： K-means clustering

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Accelerating Sample-based GNN Training by Feature Caching on GPUs 7

Accelerating Sample-based GNN Training by Feature Caching on...

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7th IEEE International Conference on Smart Cloud, SmartCloud 2022

作者： He, Yuqi Lai, Zhiquan Ran, Zhejiang Zhang, Lizhi Li, Dongsheng National University of Defense Technology National Key Laboratory of Parallel and Distributed Processing College of Computer Changsha China

ISBN: (数字)9781665451796

ISBN: (纸本)9781665451796

The existing graph neural network (GNN) systems adopt sample-based training on large-scale graphs over multiple GPUs. Although they support large-scale graph training, large data loading overhead is still a bottleneck. In this work, we propose SCGraph, a method that supports GPU high-speed feature caching. We classify the graph vertices sorted by out-degrees. For high out-degree vertices, we set grading caches via different GPUs to increase the overall cache content through NVLink high-speed data transmission between them. For low out-degree vertices, we expand 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 two datasets Reddit and ogbn-products. Experimental results show that SCGraph achieves up to 1.83× performance speedup over the state-of-the-art baselines. © 2022 IEEE.

关键词： Graph neural networks

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