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parallel Vertex Color Update on Large Dynamic networks

Parallel Vertex Color Update on Large Dynamic Networks

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international conference on High Performance computing

作者： Arindam Khanda Sanjukta Bhowmick Xin Liang Sajal K. Das Missouri University of Science and Technology Rolla USA University of North Texas Denton USA

We present the first GPU-based parallel algorithm to efficiently update vertex coloring on large dynamic networks. For single GPU, we introduce the concept of loosely maintained vertex color update that reduces computation and memory requirements. For multiple GPUs, in distributed environments, we propose priority-based ordering of vertices to reduce the communication time. We prove the correctness of our algorithms and experimentally demonstrate that for graphs of over 16 million vertices and over 134 million edges on a single GPU, our dynamic algorithm is as much as 20x faster than state-of-the-art algorithm on static graphs. For larger graphs with over 130 million vertices and over 260 million edges, our distributed implementation with 8 GPUs produces updated color assignments within 160 milliseconds. In all cases, the proposed parallel algorithms produce comparable or fewer colors than state-of-the-art algorithms.

关键词： Image color analysis Heuristic algorithms High performance computing Memory management Graphics processing units parallel algorithms Kernel

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K-means ++ and Reinforcement Learning-Based Clustering Method to Achieve Energy Efficiency in Underwater Acoustic Sensor networks

K-means ++ and Reinforcement Learning-Based Clustering Metho...

引用

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

作者： Tao Xiong Rui Hou Lingyun Zhou Guanglin Xing Deze Zeng College of Computer Science South-Central Minzu University Wuhan China Computer School China University of Geosciences Wuhan China

In recent years, underwater acoustic sensor networks (UASNs) have had a wide range of applications in the fields of underwater environment sensing, marine resource exploitation and water quality analysis. Sensor nodes operate in an underwater environment and are powered by embedded batteries, making it very difficult to replenish power. It is thus important to efficiently improve sensor energy efficiency in UASNs and to extend the network lifetime. Clustering can effectively reduce UASN energy consumption by dividing sensor nodes into clusters; however, energy efficiency is always affected by the imbalance in energy consumption due to the unreasonable selection of cluster heads (CHs) and routes. In this paper, a K-means ++ and reinforcement learning-based clustering (KRC) method is proposed to improve energy efficiency. First, the UASN clusters are uniformly distributed using the K-means ++ algorithm. Then, a combination of clustering and reinforcement learning is applied to CH selection, which calculates the energy of intercluster data transmission when each node in the cluster is a CH based on the node’s residual energy and location and selects the appropriate CHs to equalize the energy consumption of each network node. The optimal transmission route constructed by reinforcement learning is used to select suitable nodes for relaying data for sensor nodes. The simulation results show that the KRC method effectively balances UASN node energy consumption and improves energy efficiency, thereby extending network survival.

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Exploring 5G for Vehicular networks

Exploring 5G for Vehicular Networks

引用

international conference on parallel distributed and Grid computing (PDGC)

作者： Meenu Khurana Ishu Sharma Gurtej Kaur Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9781665454025

The ongoing revolution in wireless communications with the advent of 5G has opened plethora of opportunities for the applications demanding high capacity, low-latency, high reliability and security. 5G is expected to cater to the diverse and stringent requirements of the wireless applications owing to better manageability, scalability, privacy, security and efficiency offered by it in comparison to earlier frameworks. Vehicular Adhoc networks (VANETs) for intelligent transportation systems, is one such application which will be benefitted the most due to its diverse requirements for the types of applications ranging from safety, comfort to commercial. We have presented our perspective on the impact of basic building blocks of 5G framework in VANETs. Firstly, the application specific requirements of VANETs have been detailed. The existing VANET standard fulfilling these requirements is listed along with its limitations. Secondly, the features and key benefits of 5G in context to VANETs have been presented. Finally, the basic building blocks of 5G including device to device technology and edge-slicing are explored in VANETs and the associated challenges are highlighted.

关键词： Wireless communication Technical requirements 5G mobile communication Network slicing Scalability Vehicular ad hoc networks Safety

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ReQ-tank: Fine-grained distributed Machine Learning Flow Scheduling Approach

ReQ-tank: Fine-grained Distributed Machine Learning Flow Sch...

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international conference on parallel and distributed Systems (ICPADS)

作者： Quanyi Xu Kedong Yan Dan Yin Chanying Huang Shan Xiao Yuxin Yang School of Cyber Science and Engineering Nanjing University of Science and Technology Jiangyin China School of Computer Science and Engineering Nanjing Univ. of Science and Technology Nanjing China School of Computer Science and Engineering Beijing Univ. of Civil Engineering and Architecture Beijing China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Fiberhome Communication Tech. Co. Wuhan China

The swift advancement of distributed computing has enhanced the support for big data and massive-scale models. Yet, delivering superior services to manage large and intricate network flows in data center networks remains a formidable challenge. In this paper, we present ReQ-tank, an intricate flow scheduling approach based on a multi-level feedback queue (MLFQ) devised to achieve flow prioritization and efficient flow scheduling. ReQ-tank employs a two-tier scheduling strategy: On the flow scheduling layer, priority queues are segmented into two categories, and the flows within high-priority queues follow a strict priority scheduling, while those in low-priority queues adhere to differential weighted Round-robin scheduling; On the packet scheduling layer, ReQ-tank modifies the priority of initially high-priority re-transmitted packets to facilitate fine-grained data packet scheduling. We carry out simulation experiments on web search workloads and data mining workloads. Experimental results demonstrate that ReQ-tank can curtail packet wait time in the network, significantly truncate the flow completion time (FCT) of delay-sensitive flows and counteract the issue of flow starvation in traditional strict priority queues. Consequently, ReQ-tank is deemed more suitable for complex distributed network applications.

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Analysis of Clustering based Hierarchical Routing Protocols for WUSN Architecture

Analysis of Clustering based Hierarchical Routing Protocols ...

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international conference on parallel distributed and Grid computing (PDGC)

作者： Yogita Singh N. S Aulakh Inderdeep K Aulakh CSIO Analytical facilities CSIR central scientific Instruments organization (CSIR-CSIO) chandigarh India Academy of scientific and Innovative Research (AcSIR) Ghaziabad India Department of Information Technology University Institute of Engineering and Technology Panjab University Chandigarh India

ISBN: (纸本)9781665454025

Exploring underwater life and its resources requires a Wireless Underground Sensor Network (WUSN). The sensor nodes can stay underwater while still establishing a wireless network with one another and other nodes. Numerous underwater monitoring applications are made possible by WUSN because of this feature, which is unavailable with a conventional wireless sensor network (WSN). Authors have made great advances in recent years in their ability to access and study the ocean's depths and resources. There are many key distinctions between the routing techniques of traditional fixed networks and those of WUSN. This paper makes an attempt to provide analysis of different cluster-based routing protocols in WUSN. The protocols such as LEACH, PEFASIS, HEED, TEEN, APTEEN, SEP, DWEHC, DEBC, EEHC, SDEEC, DDEEC, WEP, ECBDA, and DCEBC are considered for comparison. Furthermore, it presents the internal architecture of WUSN and its types, challenges, and applications. The comparison between various clustering protocols has been analyzed based on various performance parameters such as mobility, energy, data aggregation, node deployment, scalability, location, data delivery etc.

关键词： Wireless sensor networks Scalability Wireless networks Oceans Computer architecture Routing Grid computing

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Analyzing the distributed training of deep-learning models via data locality 29

Analyzing the distributed training of deep-learning models v...

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29th Euromicro international conference on parallel, distributed and Network-Based Processing, PDP 2021

作者： Alonso-Monsalve, Saúl Calderón, Alejandro García-Carballeira, Félix Rivadeneira, José Cern Geneva Switzerland Universidad Carlos Iii de Madrid Leganés Madrid Spain Universidad Carlos Iii de Madrid Computer Science and Engineering Department Leganés Madrid Spain

ISBN: (纸本)9781665414555

In the last few years, deep-learning models are becoming crucial for numerous scientific and industrial applications. Due to the growth and complexity of deep neural networks, researchers have been investigating techniques to train those networks more efficiently. Many efforts have been made to optimize deep-learning models by parallelizing or distributing their training computation across multiple devices. Current state-of-the-art techniques, such as Horovod, have shown to maximize the performance of both the training computation and the inter-node communication of models for different deep-learning frameworks. However, some applications cannot take advantage of the above techniques due to an I/O bottleneck caused by the input data, thus limiting the scalability of the trainings. In this paper, we study an approach based on data locality - that has not been fully studied yet - for those neural networks that cannot benefit from scaling their computation due to a significant bottleneck in the data I/O. © 2021 IEEE.

关键词： Deep neural networks

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Cooperative distributed Deep Neural Network Deployment with Edge computing

Cooperative Distributed Deep Neural Network Deployment with ...

引用

IEEE international conference on Communications (ICC)

作者： Yang, Cian-You Kuo, Jian-Jhih Sheu, Jang-Ping Zheng, Ke-Jun Natl Tsing Hua Univ Dept Comp Sci Hsinchu Taiwan Natl Chung Cheng Univ Dept Comp Sci & Informat Engn Chiayi Taiwan Natl Tsing Hua Univ Dept Informat Syst & Applicat Hsinchu Taiwan

ISBN: (纸本)9781728171227

Deep Neural networks (DNNs) are widely used to analyze the abundance of data collected by massive Internet-of-Thing (IoT) devices. The traditional approaches usually send the data to the cloud and process the DNN inference on the powerful cloud servers but suffer from long network latency. Therefore, edge computing has emerged to reduce network latency by offloading the computation from the cloud to the edge. However, a single resource-constrained edge device is unable to process real-time DNN inference. Thus, we devise a collaborative edge computing system CoopAI to distribute DNN inference over several edge devices with a novel model partition technique to allow the edge devices to prefetch the required data in advance to compute the inference cooperatively in parallel without exchanging data. Subsequently, we present a new optimization problem to minimize the completion time of distributed DNN inference. An innovative algorithm is then proposed to intelligently partition the model into the proper number and sizes of blocks, deploy them on a suitable number of edge devices, and run them in different rounds. The numerical results manifest that our algorithm outperforms the traditional approach by 20%-30% on the completion time.

关键词： distributed deep neural network edge computing dynamic programming

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KAISA: An Adaptive Second -Order Optimizer Framework for Deep Neural networks 21

KAISA: An Adaptive Second -Order Optimizer Framework for Dee...

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international conference for High Performance computing, Networking, Storage and Analysis (SC21)

作者： Pauloski, J. Gregory Huang, Qi Huang, Lei Venkataraman, Shivaram Chard, Kyle Foster, Ian Zhang, Zhao Univ Chicago Chicago IL 60637 USA Univ Texas Austin Austin TX 78712 USA Texas Adv Comp Ctr Austin TX USA Univ Wisconsin Madison WI USA Argonne Natl Lab Argonne IL 60439 USA

ISBN: (纸本)9781450384421

Kronecker-factored Approximate Curvature (K-FAC) has recently been shown to converge faster in deep neural network (DNN) training than stochastic gradient descent (SCD);however, K-FAC's larger memory footprint hinders its applicability to large models. We present KAISA, a K-FAC-enabled, Adaptable, Improved, and ScAlable second -order optimizer framework that adapts the memory footprint, communication, and computation given specific models and hardware to improve performance and increase sea lability. We quantify the tradeoffs between' memory arid communication cost and evaluate KAISA on large models, including ResNet-50, Mask R-CNN, U-Net, arid BERT, on up to 128 NVIDIA A100 CPUs. Compared to the original optimizers, KAISA converges 18.1-36.3% faster across applications with the same global batch size. Under a fixed memory budget, KAISA converges 32.5% and 41.6% faster in ResNel-50 and BERT-Large, respectively. KAISA can balance memory arid communication to achieve scaling efficiency equal to or better than the baseline optimizers.

关键词： Machine Learning distributed computing Second-Order Optimization K-FAC Data-parallel Algoritluns

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Quad based Sub-Cluster Head Selection for Energy Efficiency in Wireless Sensor networks

Quad based Sub-Cluster Head Selection for Energy Efficiency ...

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international conference on parallel distributed and Grid computing (PDGC)

作者： Bhawnesh Kumar Ashwani Kumar Harendra Singh Negi Department of Computer Science and Engineering Graphic Era Deemed To Be University Dehradun India Department of Computer Science and Engineering Shri Ram Group of Colleges Muzaffarnagar India

ISBN: (纸本)9781665454025

Cluster heads are the primary consideration used in Wireless Sensor networks (WSN) to transfer data from one point to another point. A wireless sensor network's energy usage can be decreased by minimizing the nodes to communicate with final point. Organizing the network into different clusters, each of which elects a node as the head of cluster, is one method for allowing information exchange between sensors. Nodes are frequently clustered into mainly non-overlapping clusters to assist scalability. In this paper, a quad based sub-clustering innovative architecture for an effective clustering in wireless sensor heads is proposed. In a quad based sub-clustering strategy using K-means clustering, each sensor node delivers data to its sub-cluster head before sending data to the base station, as opposed to directly sending data to cluster heads. This strategy is used to reduce node power usage. The framework for a wireless sensor network's energy conservation is the subject of this research article. The framework is designed so that the sub-cluster head will be chosen after the nodes have been grouped together. The energy consumption of quad based sub-clustering is relatively less than single clustering. Through this approach, can increase the energy level of sensor networks.

关键词： Wireless communication Wireless sensor networks Energy consumption Base stations Scalability Energy conservation Grid computing

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distributed and parallel Sparse computing for Very Large Graph Neural networks

Distributed and Parallel Sparse Computing for Very Large Gra...

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IEEE international conference on Big Data

作者： Quentin R. Petit Chong Li Nahid Emad Huawei Paris Research Center & Université Paris-Saclay Boulogne-Billancourt France Distributed and Parallel Software Lab Huawei Paris Research Center Boulogne-Billancourt France Maison de la Simulation & LI-PaRAD Université Versailles Saint-Quentin Saclay France

ISBN: (纸本)9781665480468

Deep learning (DL) requires high-performance processing on big data. Graph Neural networks, a challenging topic in DL using linear algebra methods, need algorithmic solutions to efficiently assign and process graph data on modern distributed and parallel machines, which are considered with mixed arithmetic and various types of tensor/matrix accelerators. Determining compression techniques for the graph’s sparse data structures is one of the key *** first objective is to design and implement a reusable parallel numerical library to resolve large neural network graphs. Our design strategy is drawn on a component-based approach and targets maximum code reuse in various parallel contexts while allowing for performance optimization. The solution could be later integrated into a DL framework like MindSpore.

关键词： Deep learning distributed databases Linear algebra Big Data parallel machines Graph neural networks Libraries

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