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Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Jin Zhao Yu Zhang Jian Cheng Yiyang Wu Chuyue Ye Hui Yu Zhiying Huang Hai Jin Xiaofei Liao Lin Gu Haikun Liu National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan China and Zhejiang-HUST Joint Research Center for Graph Processing Zhejiang Lab Hangzhou China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798350323481

Temporal graph processing is used to handle the snapshots of the temporal graph, which concerns changes in graph over time. Although several software/hardware solutions have been designed for efficient temporal graph processing, they still suffer from serious irregular data access due to the uncoordinated graph traversal. To overcome these limitations, this paper proposes Sagraph, a domain-specific hardware accelerator to support the efficient processing of temporal graph. Specifically, temporal graph processing shows strong data access similarity, i.e., most graph accesses of the processing of different snapshots are the same and usually refer to a small fraction of vertices. Sagraph can dynamically coordinate the graph traversals and adaptively cache the vertex states to fully exploit the data access similarity for smaller data access overhead. We implemented and evaluated Sagraph on a Xilinx Alveo U280 FPGA card. Compared with the cutting-edge software and hardware solutions, Sagraph achieves 8.5×-157.3×, 4.2×-16.1× speedups and 34.7×-423.6×, 5.3×-14.7× energy savings, respectively.

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Vehicle-Assisted Service Caching for Task Offloading in Vehicular Edge computing

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IEEE Transactions on Mobile computing 2025年

作者： Jiang, Hongbo Cai, Jianghao Xiao, Zhu Yang, Kehua Chen, Hongyang Liu, Jiangchuan Hunan University College of Computer Science and Electronic Engineering Hunan Changsha China Hunan University Shenzhen Research Institute Shenzhen518055 China Zhejiang Laboratory Research Center for Graph Computing Hangzhou311100 China Simon Fraser University School of Computing Science BurnabyBCV5A 1S6 Canada

The development of artificial intelligence (AI) enables vehicular edge computing (VEC) servers to be able to provide more intelligent services. However, the limited storage resources of VEC servers constrain the deployment of intelligent service contents, which greatly restricts the intelligence level of the VEC network. To resolve this problem, we first design a novel vehicle-assisted VEC network architecture and further propose VaCo, a Vehicle-assisted Collaborative caching system. VaCo allows VEC servers to download the cached service content from any vehicle in the VEC network to support task offloading. VaCo mainly considers the real-time scheduling problem of vehicle storage resources under the dynamic VEC network and the benefit problem caused by invoking vehicle resources under the highly dynamic load environment. VaCo models the vehicle storage resources as an independent resource pool and deploys a cross-VEC server content retrieval mechanism to achieve unified and efficient management of the storage resources of the vehicle cluster and the VEC server cluster. Then, we propose a multi-swarm collaborative optimization scheme to jointly optimize the service failure rate and cost, and further propose a Pareto-based optimization scheme to ensuring that VaCo can correctly evaluate the benefits of invoking vehicle resources in a dynamic VEC network. Finally, we implement VaCo and conduct extensive evaluations on real-world dataset. The experimental results on the real trajectory dataset show that VaCo can effectively utilize vehicle resources and ensure the benefits of both vehicles and VEC servers simultaneously. © 2002-2012 IEEE.

关键词： Mobile edge computing

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PSMiner: A Pattern-Aware Accelerator for High-Performance Streaming graph Pattern Mining 23

PSMiner: A Pattern-Aware Accelerator for High-Performance St...

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Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Hao Qi Yu Zhang Ligang He Kang Luo Jun Huang Haoyu Lu Jin Zhao Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China and Zhejiang-HUST Joint Research Center for Graph Processing Zhejiang Lab China Department of Computer Science University of Warwick United Kingdom

ISBN: (纸本)9798350323481

Streaming graph Pattern Mining (GPM) has been widely used in many application fields. However, the existing streaming GPM solution suffers from many unnecessary explorations and isomorphism tests, while the existing static GPM ones require many repetitive operations to compute the full graph. In this paper, we propose a pattern-aware incremental execution approach and design the first streaming GPM accelerator called PSMiner, which integrates multiple optimizations to reduce redundant computation and improve computing efficiency. We have conducted extensive experiments. The results show that compared with the state-of-the-art software and hardware solutions, PSMiner achieves the average speedups of 770.9× and 60.4×, respectively.

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DCGG: A Dynamically Adaptive and Hardware-Software Coordinated Runtime System for GNN Acceleration on GPUs

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IEEE Transactions on Computers 2025年

作者： Xiao, Guoqing Xia, Li Chen, Yuedan Chen, Hongyang Yang, Wangdong Hunan University College of Computer Science Changsha410082 China Hunan University Electronic Engineering Changsha410082 China Hunan University Shenzhen Institute Shenzhen518063 China Central South University Big Data Institute Changsha410083 China Zhejiang Lab Research Center for Graph Computing Hangzhou311121 China

graph neural networks (GNNs) are a prominent trend in graph-based deep learning, known for their capacity to produce high-quality node embeddings. However, the existing GNN framework design is only implemented from the algorithm level, and the hardware architecture of the GPU is not fully utilized. To this end, we propose DCGG, a dynamic runtime adaptive framework, which can accelerate various GNN workloads on GPU platforms. DCGG has carried out deeper optimization work mainly in terms of load balancing and software and hardware matching. Accordingly, three optimization strategies are proposed. First, we propose dynamic 2D workload management methods and perform customized optimization based on it, effectively reducing additional memory operations. Second, a new slicing strategy is adopted, combined with hardware features, to effectively improve the efficiency of data reuse. Third, DCGG uses the Quantitative Dimension Parallel Strategy to optimize dimensions and parallel methods, greatly improving load balance and data locality. Extensive experiments demonstrate that DCGG outperforms the state-of-the-art GNN computing frameworks, such as Deep graph Library (up to 3.10× faster) and GNNAdvisor (up to 2.80× faster), on mainstream GNN architectures across various datasets. © 1968-2012 IEEE.

关键词： graph neural networks

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AC loss mitigation for high temperature superconducting coils in wireless power transfer

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Superconductivity 2023年第2期6卷 6-24页

作者： Hongyi Chen Hongye Zhang Research Center for Graph Computing Zhejiang LabHangzhouChina Department of Electrical and Electronic Engineering University of ManchesterManchester M139PLUnited Kingdom

With the rapid development of high temperature superconducting(HTS)technology,second generation(2G)HTS materials have become a promising alternative to traditional conductive materials in the power transmission ***,the topic of using HTS materials in wireless power transfer(WPT)systems for electric vehicles(EVs)has attracted widespread attention in the background of net zero *** virtually zero DC resistance and superior current‐carrying capacity,HTS materials can achieve high quality factor and power density in the WPT resonant circuits compared to conventional metals,e.g.,***,the optimal working frequency for the conventional WPT system is relatively high in the order of kilohertz *** coils working at high frequencies could generate high AC losses,reducing the overall power transfer efficiency(PTE)and increasing the cooling *** order to improve the PTE of HTS‐WPT systems,the AC loss mitigation methods for different HTS coil topologies have been investigated in this paper by varying the inter‐turn gap and tape *** HTS coil structures,namely the spiral coil,the solenoid coil and the double pancake(DP)coil,have been studied with a 2D axisymmetric multi‐layer numerical model based on the H‐formulation,and the simulation results have been validated by the published experimental *** general loss characteristics,loss distributions in each turn,as well as magnetic flux densities have been analysed in detail for three types of HTS ***,the impact of these two loss reduction methods on the WPT performance has also been *** have shown that increasing the inter‐turn gap and tape width can effectively reduce the AC power losses and increase the PTE of the HTS‐WPT *** spiral coil demonstrates the highest AC power loss reduction effect and PTE while maintaining a stable level of magnetic *** paper is believed to deepen the understanding of superconducting WPT and provi

关键词： Wireless Power Transfer(WPT) High temperature superconductor Loss reduction Inter‐turn gap Tape width

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Deep learning based tracking reconstruction and magnetic field measurement research in the muon g-2 experiment

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Nuclear and Particle Physics Proceedings 2024年 345卷 18-21页

作者： Li, Bingzhi Lü, Shuotian Research Center for Graph Computing Zhejiang Lab Hangzhou 310000 China School of Physics and Astronomy Shanghai Jiao Tong University Shanghai 200240 China

The Run1 result of the Fermilab muon g-2 experiment have shown a 4.2 standard deviation between the experimental measurement and theoretical prediction of aμ, strongly indicating a new physics signal. The Fermilab ex... 详细信息

The Run1 result of the Fermilab muon g-2 experiment have shown a 4.2 standard deviation between the experimental measurement and theoretical prediction of a_μ, strongly indicating a new physics signal. The Fermilab experiment already accumulated 21 times more data compared to the BNL experiment. The J-PARC muon g-2 experiment will collect 3.5 times the statistics compared to Fermilab. With the increases in the collected data volume, and limited by the speed and accuracy, the existing tracking reconstruction and magnetic field measurement method may not fully satisfy the requirement of the experiment. The breakthrough of the deep learning inspires new analysis method in the muon g-2 experiment. In this proceeding, we will present some preliminary research on the tracking reconstruction based on Recurrent Neural Network (RNN), graph Neural Network (GNN) and the magnetic field measurement based on Physics Informed Neural Network (PINN). The preliminary results show that the deep learning method has enormous potential in these topics. © 2024 Elsevier B.V.

关键词： Deep learning Magnetic field measurement Muon g-2 experiment Tracking reconstruction

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Circle Feature graphormer: Can Circle Features Stimulate graph Transformer?

arXiv

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arXiv 2023年

作者： Lv, Jingsong Chen, Hongyang Qi, Yao Yu, Lei Research Center for Graph Computing Zhejiang Lab Zhejiang Province Hangzhou China

In this paper, we introduce two local graph features for missing link prediction tasks on ogbl-citation2. We define the features as Circle Features, which are borrowed from the concept of circle of friends. We propose the detailed computing formulas for the above features. Firstly, we define the first circle feature as modified swing for common graph, which comes from bipartite graph. Secondly, we define the second circle feature as bridge, which indicates the importance of two nodes for different circle of friends. In addition, we firstly propose the above features as bias to enhance graph transformer neural network, such that graph self-attention mechanism can be improved. We implement a Circled Feature aware graph transformer (CFG) model based on SIEG network, which utilizes a double tower structure to capture both global and local structure features. Experimental results show that CFG achieves the state-of-the-art performance on dataset ogbl-citation2. Copyright © 2023, The Authors. All rights reserved.

关键词： graph neural networks

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Path-aware Siamese graph Neural Network for Link Prediction

arXiv

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arXiv 2022年

作者： Lv, Jingsong Li, Zhao Chen, Hongyang Qi, Yao Wu, Chunqi Research Center for Graph Computing Zhejiang Lab Zhejiang Province Hangzhou China

In this paper, we propose an algorithm of Path-aware Siamese graph neural network(PSG) for link prediction tasks. First, PSG captures both nodes and edge features for given two nodes, namely the structure information of k-neighborhoods and relay paths information of the nodes. Furthermore, a novel multi-task GNN framework with self-supervised contrastive learning is proposed for differentiation of positive links and negative links while content and behavior of nodes can be captured simultaneously. We evaluate the proposed algorithm PSG on two link property prediction datasets, ogbl-ddi and ogbl-collab. PSG achieves top 1 performance on ogbl-ddi until submission and top 3 performance on ogbl-collab. The experimental results verify the superiority of our proposed PSG. Copyright © 2022, The Authors. All rights reserved.

关键词： graph neural networks

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GALAS: Greedy Local-Adjusting Room Allocation Framework 9

GALAS: Greedy Local-Adjusting Room Allocation Framework

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9th IEEE Smart World Congress, SWC 2023

作者： Chi, Lian Xie, Jiajia Wu, Jinxin Chen, Hongyang Zhao, Tianming Nanjing University of Information Science and Technology School of Artificial Intelligence Nanjing China Hunan University School of Computer Science and Electronic Engineering Changsha China Research Center for Graph Computing Zhejiang Lab Hangzhou China

ISBN: (纸本)9798350319804

In this paper, we study the room allocation problem to optimize room utilization in allocating rooms for courses under all the conflicting constraints, which has been proven NP-complete. We first formulate the problem as an Integer Linear Program (ILP) so it can be solved optimally using an ILP solver. However, solving ILP incurs an exponential run-time complexity, which is impractical for real-world applications. To address this issue, we propose Greedy Algorithm plus Local Adjustment via Swaps (GALAS), a greedy heuristic framework with a reduced run-time that finds near-optimal room allocation solutions. GALAS uses multiple greedy strategies to quickly generate feasible schedules and then optimize them via a sequence of local swap operations. We evaluate the performance of the GALAS on synthetic and real-world datasets. The experimental results show that the GALAS outperforms the state-of-the-art in room utilization and run-time simultaneously. On average, GALAS achieves a near-optimal performance of 98.5% to the optimal solution. © 2023 IEEE.

关键词： Heuristic Local adjustment Room allocation

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PatternRCA: A Pattern-Aware Root Cause Analysis Framework for Multi-Dimensional Time Series 23

PatternRCA: A Pattern-Aware Root Cause Analysis Framework fo...

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23rd IEEE International Conference on Data Mining, ICDM 2023

作者： He, Cheng Tian, Fulong Xue, Peijiao Wu, Yuan Li, Yao Li, Jiajia Wang, Zikai Tan, Feng Chen, Hongyang Kong, Linghe Information Technology Co. Ltd Shanghai China School of Chemistry and Chemical Engineering National Center for Translational Medicine Shanghai Jiao Tong University Shanghai China Shanghai Artificial Intelligence Research Institute Shanghai China Research Center for Graph Computing Zhejiang Lab China Shanghai Jiao Tong University Shanghai China

Root cause analysis for multi-dimensional time series from large scale micro-service scenarios aims at identifying the set of anomaly attributes by monitoring operational metrics. The online metrics provide a general indication to investigate these attributes' inter-dependencies and can guide the overall exploration process. However, the problem space for the root cause localization still remains largely challenging due to the combinatorial explosion of possible attribute combinations. This leads researchers and practitioners to (a) assume some prior distributions on the data set;(b) assume some data patterns on the attribute combinations;(c) perform pruning techniques to reduce the search space. Furthermore, state-of-the-art root cause analysis methods are often tied to one or more of these assumptions, which makes it difficult to be robust to general scenarios. In this paper, we conclude the heterogeneity in the data patterns by analyzing several open and industrial datasets. A uniform analytical framework, PatternRCA, is proposed such that it can be aware of the patterns in the metrics while avoiding explicit assumptions about them. We design an offline learning procedure that enables the framework to detect existing data patterns, which then can guide it to do fine-grain exploration in online metrics. Our extensive evaluation results show that PatternRCA outperforms state-of-the-art models with better benchmark results in public datasets. Meanwhile, it can scale to complex root cause analysis tasks on datasets with hybrid patterns in production environments. © 2023 IEEE.

关键词： Time series

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