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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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Topology Identification Technology for Low-Voltage distributed Photovoltaic Distribution Network Lines

Topology Identification Technology for Low-Voltage Distribut...

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Energy Engineering and Power Systems (EEPS), international conference on

作者： Guoquan Zheng Enguo Zhu Hailong Zhang Yan Liu Junlong Wang Zhe Liu China Electric Power Research Institute Beijing China State Grid Hebei Electric Power Beijing China

ISBN: (数字)9798350375138

ISBN: (纸本)9798350375145

The large-scale penetration of distributed photovoltaic (PV) power generation systems has brought new challenges to the topology identification and detection of traditional distribution networks. This article mainly studies the topology identification technology (TIT) of distributed PV low-voltage (LV) distribution network lines, aiming to design a topology identification method that can adapt to the dynamic changes of the power grid, have large-scale capacity, and improve system accuracy under the same conditions. At the same time, the data processing speed has been improved. This article first constructs a system model, including node model, edge model, and parameter model. It mathematically represents the topology structure of the power grid using graph theory and designs a topology recognition algorithm based on optimization techniques and state estimation. This algorithm is used to solve the distributed characteristics of power grid topology recognition, the difficulty of data collection, the dynamic diversity of power grid structure, the uncertainty of equipment parameters, the high computational complexity of data processing, and the communication constraints of power grid topology recognition. This algorithm adopts modern programming languages and parallel computing frameworks, making it easy to implement efficiently. The results on the simulation platform show that the highest recall rate for 22 test cases is 93.8%, and the response time for test cases is 425 ms to 980 ms, providing a fast response to the information space of the grid.

关键词： Photovoltaic systems Adaptation models Low voltage Network topology Heuristic algorithms Power system dynamics Distribution networks Power grids Mathematical models Topology

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Minimizing Age of Information for distributed Control in Smart Grids

Minimizing Age of Information for Distributed Control in Sma...

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2021 IEEE international conference on Communications, Control, and computing Technologies for Smart Grids, SmartGridComm 2021

作者： Fisser, Leonard Timm-Giel, Andreas Germany

ISBN: (纸本)9781665415026

Exchanging information between actively-managed components is key for establishing distributed control schemes in future Low Voltage Distribution Grids (LVDGs). Each node's measurements and control data has to be disseminated to all other nodes in the electrical grid, to allow for safe operation and control. However, periodic all-to-all flooding procedures are challenging for wireless communication networks and especially so, if end-to-end reliability is required. The effectiveness of a flooding protocol is well captured in the Age of Information (AoI) performance indicator, combining the time it took to disseminate a data chunk and the time in between updates. Existing flooding protocols are not concerned with keeping AoI low in scenarios where status updates have to be continuously distributed. We propose a novel parallel Sequential All-to-All Flooding (PSAA) protocol which is tailored to LVDGs and tries to minimize the average AoI. Special focus is given to the relation between AoI and retransmissions which are necessitated by unreliable communication channels. We show that PSAA is able to significantly outperform simple flooding schemes in characteristic LVDG topologies. Extensive simulation studies highlight the interaction between retransmission timer and AoI. © 2021 IEEE.

关键词： Electric power transmission networks

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A Physics and Data-Driven Hybrid PINNs Intelligent computing Method for Nuclear Engineering Simulation

A Physics and Data-Driven Hybrid PINNs Intelligent Computing...

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Electronic Information Engineering and Computer Science (EIECS), 2021 international conference on

作者： Yufei Xie Wenlin Wang Guohua Wu Yang Yu Ping An Zibin Sun Haichuan Zhang Shengfeng Luo Yue Li School of Automation Wuhan University of Technology Wuhan China Sino-German College of Intelligent Manufacturing Shenzhen Technology University Shenzhen China Nuclear Power Institute of China Chengdu China National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology Changsha China

ISBN: (数字)9798331531409

ISBN: (纸本)9798331531416

In the field of nuclear energy, the Loss of Coolant Accident (LOCA) is recognized as one of the most severe types of nuclear reactor accidents, characterized by its complex physical processes and potentially catastrophic consequences. These challenges impose stringent requirements on safety analysis and emergency response. Accurate prediction and analysis of fluid behavior within pipelines under LOCA conditions are critical for evaluating accident outcomes and formulating response strategies. This paper introduces an innovative intelligent computing approach—a Physics-Informed Neural networks (PINNs) model driven by both physical data and simulation data, specifically tailored for LOCA conditions. To address the challenges posed by multivariable and complex physical relationships, the six-equation two-fluid model is first simplified to represent the physical processes. Subsequently, a dual-driven PINNs network is developed, integrating both simulation data and physical constraints. The proposed model demonstrates a root mean square error (RMSE) of 0.02, a mean absolute error (MAE) of 0.044, and an R2 of 0.81 in predicting the outcomes under the six-equation model.

关键词： Solid modeling Accuracy Fluids Nuclear energy Computational modeling Pipelines Neural networks Predictive models Numerical models Accidents

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Exploration on Facial Image Recognition and Processing Algorithms under Hadoop

Exploration on Facial Image Recognition and Processing Algor...

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Integrated Intelligence and Communication Systems (ICIICS), international conference on

作者： Meifang Gu Hailin Tang Lili Li Institutional Research Center Guangdong Baiyun University Guangzhou Guangdong China Faculty of Intelligent Manufacturing Engineering Guangdong Baiyun University Guangzhou Guangdong China Faculty of Megadata and Computing Guangdong Baiyun University Guangzhou Guangdong China

Face recognition is an important technology in the field of digital image processing, which has a crucial impact on subsequent work. This article introduced an example of facial recognition based on the combination of Hadoop algorithm and facial feature extraction. The experimental results were validated in the MATLAB environment and trained using neural networks to improve system performance and adaptability. The test results indicated that the processing speed was between 0.84 and 0.97; the highest resource utilization rate was 0.06, and the lowest was 0.02; the throughput range of facial data was between 13598 and 15479, and the throughput range of user data was between 5037 and 5879. This indicated that the system had fast recognition speed and high-precision image output, and exhibited good fault tolerance and robustness compared to other methods. Exploring facial image recognition and processing algorithms in the Hadoop environment can utilize Hadoop’s distributed computing capabilities to accelerate the processing of large-scale facial image data, achieve fast and efficient processing of large-scale facial image data, accelerate algorithm operation, and possess distributed computing capabilities. Multiple computing nodes can be used for parallel processing to improve algorithm performance.

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Deep Reinforcement Learning to Enhance the Energy-efficient Performance of Unmanned Aerial Vehicles-enabled Fog Radio Access networks

Deep Reinforcement Learning to Enhance the Energy-efficient ...

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IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Chuanjie Liu Siyu Ren Shuang Du Cheng Dai Bing Guo Runzhi Fan School of Computer Science Sichuan University Chengdu China School of Aeronautic and Astronautic UESTC Chengdu China

Nowadays, the use of unmanned aerial vehicles (UAVs) as the fog access points (F-APs) is of high practical value to future fog radio access networks (F-RANs). Compared to the F-AP, UAV enabled F-AP possesses stronger line-of-sight links with the ground terminals (GTs) due to its high altitude as well as high and flexible mobility in 3D spaces. Nevertheless, unlike terrestrial F-AP that has reliable power supply, UAV- enabled F-AP in practice has limited on board power, but requires high propulsion power to stay air-borne and support high mobility. Motivated by the above considerations, this paper aims to explore how deep reinforcement learning (DRL) can be applied to enhance the energy-efficient performance of UAV- enabled F-RANs. Such DRL method supports UAV enabled F-AP to real-timely optimize the air-to-ground fog computing by reasoning network configuration in an online manner, which sheds new light on the existing works in UAV-enabled networks. In specific, the DRL based network configuration considers parameters including resource allocation, task offloading, cache deployment and 3D UAV trajectory, and get validated via simulation in this paper. In the end, the main design challenges and promising directions for future research are also discussed.

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Near-Linear Scaling Data parallel Training with Overlapping-Aware Gradient Compression

Near-Linear Scaling Data Parallel Training with Overlapping-...

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international conference on parallel and distributed Systems (ICPADS)

作者： Lin Meng Yuzhong Sun Weimin Li Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences

Existing Data parallel (DP) trainings for deep neural networks (DNNs) often experience limited scalability in speedup due to substantial communication overheads. While Overlapping technique can mitigate such problem by paralleling communication and computation in DP, its effectiveness is constrained by the high communication-to-computation ratios (CCR) of DP training tasks. Gradient compression (GC) is a promising technique to obtain lower CCR by reducing communication volume directly. However, it is challenging to obtain real performance improvement by applying GC into Overlapping because of (1) severe performance penalties in traditional GCs caused by high compression overhead and (2) decline of Overlapping benefit owing to the possible data dependency in GC schemes. In this paper, we propose COVAP, a novel GC scheme designing a new coarse-grained filter, makes the compression overhead close to zero. COVAP ensures an almost complete overlap of communication and computation by employing adaptive compression ratios and tensor sharding tailored to specific training tasks. COVAP also adopts an improved error feedback mechanism to maintain training accuracy. Experiments are conducted on Alibaba Cloud ECS instances with different DNNs of real-world applications. The results illustrate that COVAP outperforms existent GC schemes in time-to-solution by 1.92x-15.39x and exhibits near-linear scaling. Furthermore, COVAP achieves best scalability under experiments on four different cluster sizes.

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GPU-Accelerated Maximal Bicliques Mining Framework for Large E-commerce networks

GPU-Accelerated Maximal Bicliques Mining Framework for Large...

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IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Jingdong Li Zhao Li Xiaoling Wang Xingjian Lu Ji Zhang Hongyang Chen East China Normal University Shanghai China Zhejiang Lab China University of Southern Queensland Australia

Many of Taobaos important daily data mining tasks, such as anomaly attack detection and interest group detection, require efficient algorithmic solutions for mining specific graph patterns. The most common graph pattern is biclique which has a very dense structure and often contains rich implicit information. An important question to address is whether and how we can efficiently find all the interesting bicliques in large e-commerce networks, which is coined as the Maximal Biclique Enumeration problem (MBE). MBE involves enumerating all the maximal bicliques in the given graph, which is rather computationally expensive for large networks. However, recent research works on MBE haven’t made good use of GPU, a very widely used high-speed computing resource. In this paper, we propose GMBE, a novel framework that achieves an efficient utilization of the power of GPUs to parallelize the MBE algorithm to find all maximal bicliques. We design a programmable API for data analysts to meet different business needs, enabling GMBE to become the middleware to effectively support various graph mining applications in e-commerce domain. Extensive experiments show that GMBE achieves significant (12X) speedup on average over the state-of-the-art MBE algorithms.

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Prototyping of Low-Cost Configurable Sparse Neural Processing Unit with Buffer and Mixed-Precision Reshapeable MAC Array

Prototyping of Low-Cost Configurable Sparse Neural Processin...

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international conference on parallel and distributed Systems (ICPADS)

作者： Binyi Wu Wolfgang Furtner Bernd Waschneck Christian Mayr Faculty of Electrical and Computer Engineering Technische Universit¨at Dresden Dresden Germany Division of Power and Sensor Systems Infineon Technologies Munich Germany Centre for Tactile Internet with Human-in-the-loop (CeTI) Technische Universität Dresden Dresden Germany

More recently, it has become possible to run deep learning algorithms on edge devices such as microcontrollers due to continuous improvements in neural network optimization algorithms such as quantization and neural architecture search. Nonetheless, most of the embedded hardware available today still falls short of the requirements of running deep neural networks. As a result, specialized processors have emerged to improve the inference efficiency of deep learning algorithms. However, most are not for edge applications that require efficient and low-cost hardware. Therefore, we design and prototype a low-cost configurable sparse Neural Processing Unit (NPU). The NPU has a built-in buffer and a reshapable mixed-precision multiply-accumulator (MAC) array. The computing and memory resources of the NPU are parameterized, and different NPUs can be derived. Besides, users can also conFigure the NPU at runtime to fully utilize the resources. In our experiments, the 200MHz NPU with only 32 MACs is more than 32 times faster than the 400MHzSTM32H7 when inferring MobileNet-Vl. Besides, the yielded NPUs can achieve roofline or even beyond roofline performance. The buffer and reshapeable MAC array push the NPU’s attainable performance to the roofline, while the feature of supporting sparsity allows the NPU to obtain performance beyond the roofline.

关键词： Deep learning Runtime Quantization (signal) Program processors Neural networks Random access memory Prototypes

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Service Discovery and Provisioning in Social Digital Twin networks: a Name-based Approach

Service Discovery and Provisioning in Social Digital Twin Ne...

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IEEE international conference on Wireless and Mobile computing, Networking And Communications (WiMob)

作者： Marica Amadeo Giuseppe Ruggeri Claudio Marche Michele Nitti university mediterranea of reggio calabria italy university of cagliari italy

ISBN: (数字)9798350387445

ISBN: (纸本)9798350387452

Digital Twin (DT) technology is expected to cover a crucial role in a variety of 6G application scenarios, including smart automotive, smart home and smart city. By leveraging advanced Artificial Intelligence (AI) modules alongside cutting-edge communication and networking architectures, DTs will be able to develop cognitive and social skills and build relationships with each other, thus facilitating the sharing of services and experience. However, in current implementations, DTs typically engage with their physical counterpart only, for predictive main-tenance and optimization, while protocols for inter-twin commu-nications and service discovery are still unexplored. In this paper, we focus on service discovery and provisioning in DT networks hosted at the network edge. In our design, the Social Internet of Things (SIoT) notion is applied to build social networks among DTs and, in parallel, name-based primitives, according to the Information Centric Networking (ICN) paradigm, are consid-ered to support inter-twin interactions. Two distributed name-based service discovery mechanisms are envisioned: a social-driven scheme, leveraging friendship and similarities among DTs' names, and a network-driven scheme, leveraging the ICN forwarding fabric only. A performance evaluation shows the benefits of the conceived solution in terms of reduced discovery latency compared to legacy centralized approaches.

关键词： Wireless communication Performance evaluation Protocols Social networking (online) Smart cities Smart homes Social Internet of Things Digital twins Artificial intelligence Optimization

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