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Job2Vec: A Self-Supervised Contrastive Learning Based HPC Job Power Consumption Prediction Framework 29

Job2Vec: A Self-Supervised Contrastive Learning Based HPC Jo...

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29th IEEE international conference on parallel and distributed Systems, ICPADS 2023

作者： Zhang, Jie Song, Jian Li, Xiang Tian, Xuesen Zhao, Zhigang Wu, Lu Wang, Chunxiao Key Laboratory of Computing Power Network and Information Security Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

In the context of the rapid development of big data and artificial intelligence, the field of HPC is facing significant challenges in energy consumption. To address this challenge, this paper proposes a power consumption prediction framework for HPC jobs based on self-supervised contrastive learning, named Job2Vec. Firstly, HPC jobs are clustered into different clusters based on job logs and power consumption curve shapes. Next, high-quality features of power consumption data at different granularities within each cluster are captured through self-supervised contrastive learning. Lastly, an incremental update strategy is introduced to handle the dynamic changes in HPC data. The experimental results demonstrate that, on the Jinan Supercomputing dataset comprising 1170 HPC jobs, the Job2Vec framework outperforms the baseline models overall in terms of prediction performance. Additionally, we also investigated the model's performance in single-step and multi-step forecasting, the impact of different indicators on prediction accuracy, and conducted ablation experiments to confirm the indispensability of each module. This is the first time that self-supervised contrastive learning has been introduced into the HPC field, aiming to provide support for energy scheduling in data centers. © 2023 IEEE.

关键词： Contrastive Learning HPC Power Consumption Prediction

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Proceedings - 3rd international conference on Smart Technologies, Communication and Robotics 2023, STCR 2023

Proceedings - 3rd International Conference on Smart Technolo...

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3rd international conference on Smart Technologies, Communication and Robotics, STCR 2023

ISBN: (纸本)9798350370867

The proceedings contain 82 papers. The topics discussed include: advanced malware analysis and prevention;design and analysis of Vivaldi antenna by an iterative method;EGFD-AROMA-based pattern forecasting with crime rate level identification;early detection and prognosis of brain tumor from micro array gene data using machine learning classifiers;handwritten digit recognition using fine-tuned convolutional neural network model;design of high gain 5G millimeter wave micro strip patch antenna for wireless applications;using machine learning regression model to predict the optimum election algorithm for parallel and distributed computing systems;a design thinking approach of metaheuristic empowerment for energy-efficient and optimized routing protocol in IoT-enabled wireless sensor networks;and an overview on IoT architecture application layer and security threats.

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dBFC: Destination-based Backpressure Flow Control for Incast 29

dBFC: Destination-based Backpressure Flow Control for Incast

引用

29th IEEE international conference on parallel and distributed Systems, ICPADS 2023

作者： Guan, Zewei Li, Wenxin He, Xin Zhang, Song Li, Keqiu Tianjin University College of Intelligence and Computing China

ISBN: (纸本)9798350330717

Incast happens in data center networks (DCNs) when many senders simultaneously send flows to one receiver. Incast occurs frequently and compromises flow performance severely. However, widely deployed end-to-end congestion control (CC) protocols are inefficient at handling incast, as they rely on delayed congestion signals and can not isolate incast flows. Recently, researchers have favored per-hop flow control protocols since they could achieve fast reaction and incast isolation. Nevertheless, these impressive advantages are impractical because of hardware resource *** this paper, we present a new per-hop flow control protocol dBFC (Destination-based Backpressure Flow Control), which achieves these advantages within limited hardware resources. Our key insight is that the number of destinations reached by queued packets in a port is minuscule. dBFC is compatible with existing CC protocols. We use large-scale NS3 simulations to evaluate dBFC. In our evaluation, compared with deployed CC protocols and the state-of-the-art per-hop flow control protocol, dBFC reduces the maximum buffer occupancy by 5 - 23× and thus provides up to 21× lower flow completion time of small flows in limited hardware resources. © 2023 IEEE.

关键词： datacenter network incast per-hop flow control

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Enhancing Resolution of Inferring Hi-C Data Integrating U-Net and ResNet networks 23rd

Enhancing Resolution of Inferring Hi-C Data Integrating U-Ne...

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23rd international conference on parallel and distributed computing, Applications, and Technologies, PDCAT 2022

作者： Zhao, Fengjiao Li, Na Zhong, Cheng School of Computer Electronics and Information Guangxi University Guangxi Nanning530004 China Key Laboratory of Parallel and Distributed Computing Technology in Guangxi Universities Guangxi Nanning530004 China

ISBN: (纸本)9783031299261

The Hi-C data is the basis of three-dimensional chromosome structure reconstruction. In this paper, a method integrating U-Net and ResNET network is proposed to infer high resolution Hi-C data from existing low-resolution Hi-C data. Firstly, the U-Net is used as the backbone network to make full use of the shallow layer details and deep layer essential features of Hi-C data. Secondly, the residual module is introduced into the coding path and decoding path of the network to increase the depth of the model and prevent network degradation. Finally, the down sampling is used to reduce the calculation amount to decrease the required running time for the network model. The experimental results on real datasets showed that the proposed method had better performance than existing methods, and the inferred high resolution Hi-C data was closer to the real Hi-C data. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Chromosomes

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AutoPipe: A Fast Pipeline parallelism Approach with Balanced Partitioning and Micro-batch Slicing

AutoPipe: A Fast Pipeline Parallelism Approach with Balanced...

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IEEE international conference on Cluster computing (CLUSTER)

作者： Liu, Weijie Lai, Zhiquan Li, Shengwei Duan, Yabo Ge, Keshi Li, Dongsheng Natl Univ Def Technol Coll Comp Natl Lab Parallel & Distributed Proc PDL Changsha Peoples R China

ISBN: (数字)9781665498562

ISBN: (纸本)9781665498562

Recently, pipeline parallelism has been widely used in training large DNN models. However, there are still two main challenges for efficient pipeline parallelism: i) a balanced model partition is crucial for pipeline efficiency, whereas prior works lack a sound solution to generate a balanced partition automatically. ii) the startup overhead is inevitable and especially significant for deep pipelines, which is an essential source of pipeline bubbles and severely affects pipeline scalability. We propose AutoPipe to solve these two problems, which contains i) a planner for automatically and quickly generating a balanced pipeline partition scheme with a fine-grained partitioner. This partitioner groups DNN in the sub-layer granularity and finds the balanced scheme with a heuristic search algorithm;and ii) a micro-batch slicer that reduces pipeline startup overhead according to the planner results by splitting the micro-batch evenly. This slicer automatically solves an appropriate number of micro-batches to split. The experimental results show that AutoPipe can accelerate training by up to 1.30x over the state-of-the-art distributed training framework Megatron-LM, with a 50% reduction in startup overhead and an order-of-magnitude reduction in pipeline planning time. Furthermore, AutoPipe Planner improves the partition balance by 2.73x-12.7x compared to DAPPLE Planner and Piper.

关键词： artificial neural networks distributed system

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A parallel software pipeline to select relevant genes for pathway enrichment 30

A parallel software pipeline to select relevant genes for pa...

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30th Euromicro international conference on parallel, distributed and Network-Based Processing (PDP)

作者： Agapito, Giuseppe Cannataro, Mario Magna Graecia Univ Catanzaro Dept Law Econ & Social Sci Catanzaro Italy Magna Graecia Univ Catanzaro Data Analyt Res Ctr Catanzaro Italy Magna Graecia Univ Catanzaro Dept Surg & Med Sci Catanzaro Italy

ISBN: (纸本)9781665469586

The continuous technological development of experimental omics technologies such as microarrays, allows to perform large scale genomics studies. After the initial enthusiasm, it became pretty clear that even the results provided by microarrays in form of lists of differential expressed genes (DEGs), were mainly as enigmatic as the first sequence of the genome, because these lists of DEGs are detached from the influenced biological mechanisms. Pathway enrichment analysis (PEA) supports researchers to provide the clues necessary to link DEGs to the influenced biological pathways and consequently to the underlying biological mechanisms and processes. Putting DEGs data sets in a suitable format for the PEA can be a tedious error-prone and laborious process even for bioinformaticians, who needs to perform it manually before to be ready for the PEA. To fill this lack, we present a parallel software pipeline which uploads a list of DEGs and automatically provides as results the enriched pathways. The parallel software pipeline is implemented in Python and provides the following automated actions: i) parallel splitting of DEGs in groups;ii) parallel building of the similarity matrices related to the DEGs groups;iii) parallel mapping of similarity matrices in networks;iv) parallel pathway enrichment analysis for each group of identified DEGs. Preliminary results shown that the pipeline can help to analyze DEGs and easily generate in a few minutes a list of pathway enrichment results that otherwise would require numerous hours of manual work and several different scripts. The parallel software pipeline provides a two-fold benefits: first, it contributes to speed up the computation of pathway enrichment, automating several steps currently performed manually. Second, it provides a more peculiar list of DEGs to calculate pathway enrichment, contributing to improve the relevance and significance of the enriched pathways.

关键词： Pathway Enrichment Analysis parallel computing Omics Sciences Statistical Analysis Network Analysis

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Research on Feature Extraction Algorithm of Graph Data Based on Graph Neural Network

Research on Feature Extraction Algorithm of Graph Data Based...

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Frontiers Technology of Information and Computer (ICFTIC), IEEE international conference on

作者： Shuang Qiu Hairong Hao Zhou Dong School of Information Engineering Eurasia University Xi'an China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

This paper proposes an optimization model that integrates Hadoop, Graph Convolutional networks (GCN), and Ant Colony Optimization (ACO) to enhance GNN performance in large-scale networks. Hadoop is utilized for distributed storage and processing of large graph datasets, dividing them into subgraphs across multiple computing nodes. The MapReduce framework accelerates the training and inference of GCN through parallel computing. GCN is employed to extract node features by performing convolution on the graph's adjacency matrix. ACO is then applied for global hyperparameter tuning and network structure optimization. By exploring the parameter space, ACO identifies the optimal configuration for GCN. This model leverages Hadoop's distributed computing power, GCN's effective feature extraction, and ACO's optimization capabilities. Experimental results demonstrate improved efficiency and performance on large-scale network datasets compared to traditional methods.

关键词： Training Graph convolutional networks Convolution Optimization models Computational modeling parallel processing Feature extraction distributed computing Optimization Tuning

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ReQ-tank: Fine-grained distributed Machine Learning Flow Scheduling Approach 29

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

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29th IEEE international conference on parallel and distributed Systems, ICPADS 2023

作者： Xu, Quanyi Yan, Kedong Yin, Dan Huang, Chanying Xiao, Shan Yang, Yuxin 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

ISBN: (纸本)9798350330717

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. © 2023 IEEE.

关键词： Data Center Networking Flow scheduling packet scheduling SDN

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Customer Insights Analysis Using Deep Learning 7

Customer Insights Analysis Using Deep Learning

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7th international conference on parallel, distributed and Grid computing, PDGC 2022

作者： Das, Parichay Singh, Vijendra University of Petroleum and Energy Studies School of Computer Science Dehradun India

ISBN: (纸本)9781665454018

The number of organizations moving their activities online or expanding their online presence to compete in the current market is growing significantly. Increasingly, organizations are turning to online marketing strategies incorporating a comprehensive consumer engagement approach. In addition to maintaining existing customers and attracting new ones, customer segmentation is a key to organizational success. However, the unstructured data is segmented using an unsupervised learning algorithm. Traditional clustering algorithms are not guaranteed to be efficient for customer segmentation and behavior analysis. Consequently, after comparing the algorithms, only the most suitable algorithm is suggested for research KMeans, or KMeans combined with other algorithms, are the basis of most algorithms, according to the literature review. However, in the general approach, KMeans algorithms cannot handle large and high-dimensional data sets;therefore, for this research, Customer segmentation will be improved using K-means and Deep Cluster networks (DCN). Further, traditional models of machine learning are not effective in analyzing the behavior of customers. Therefore, during the present study, a Deep Cluster networks (DCN)-based architecture has also been used to produce effective results. Nowadays, customer Insights Analysis Deep Learning plays a very vital role. © 2022 IEEE.

关键词： Deep neural networks

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FedCD: A Hybrid Centralized-Decentralized Architecture for Efficient Federated Learning 29

FedCD: A Hybrid Centralized-Decentralized Architecture for E...

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29th IEEE international conference on parallel and distributed Systems, ICPADS 2023

作者： Qu, Pengcheng Liu, Jianchun Wang, Zhiyuan Ma, Qianpiao Huang, Jinyang University of Science and Technology of China School of Computer Science and Technology Hefei China Purple Mountain Laboratories Nanjing China Hefei University of Technology School of Computer and Information Hefei China

ISBN: (纸本)9798350330717

With billions of IoT devices producing vast data globally, privacy and efficiency challenges arise in AI applications. Federated learning (FL) has been widely adopted to train deep neural networks (DNNs) without privacy leakage. Existing centralized and decentralized FL architectures have limitations, including memory burden, huge bandwidth pressure and non-IID data issues. This paper introduces a novel framework, named FedCD, merging the benefits of both centralized and decentralized FL architectures. FedCD strategically distributes the model based on layer sizes and consensus distances (measuring the deviation between the local models and the global average models), effectively relieving network bandwidth pressures and accelerating training speed even under the non-IID setting. This method significantly mitigates resource constraints and improves model accuracy, offering a promising solution to the challenges in distributed machine learning. Extensive experiment results show the high effectiveness of FedCD. The total completion time of FedCD is reduced by 16.3%-53% and the average accuracy improvement is 1.85% compared to the existing FL systems. © 2023 IEEE.

关键词： edge computing federated learning layer distribution

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