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A Fuzzy Neural Network Based Dynamic Data Allocation Model on Heterogeneous Multi-GPUs for Large-scale Computations

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International Journal of Automation and computing 2018年第2期15卷 181-193页

作者： Chao-Long Zhang Yuan-Ping Xu Zhi-Jie Xu Jia He Jing Wang Jian-Hua Adu school of software engineering Chengdu University of Information Technology school of Computing & engineering University of Huddersfield school of Computer Science Chengdu University of Information Technology Department of Computing Sheffield Hallam University

The parallel computation capabilities of modern graphics processing units （GPUs） have attracted increasing attention from researchers and engineers who have been conducting high computational throughput studies. However, current single GPU based engineering solutions are often struggling to fulfill their real-time requirements. Thus, the multi-GPU-based approach has become a popular and cost-effective choice for tackling the demands. In those cases, the computational load balancing over multiple GPU ＂nodes＂ is often the key and bottleneck that affect the quality and performance of the real=time system. The existing load balancing approaches are mainly based on the assumption that all GPU nodes in the same computer framework are of equal computational performance, which is often not the case due to cluster design and other legacy issues. This paper presents a novel dynamic load balancing （DLB） model for rapid data division and allocation on heterogeneous GPU nodes based on an innovative fuzzy neural network （FNN）. In this research, a 5-state parameter feedback mechanism defining the overall cluster and node performance is proposed. The corresponding FNN-based DLB model will be capable of monitoring and predicting individual node performance under different workload scenarios. A real=time adaptive scheduler has been devised to reorganize the data inputs to each node when necessary to maintain their runtime computational performance. The devised model has been implemented on two dimensional （2D） discrete wavelet transform （DWT） applications for evaluation. Experiment results show that this DLB model enables a high computational throughput while ensuring real=time and precision requirements from complex computational tasks.

关键词： Heterogeneous GPU cluster dynamic load balancing fuzzy neural network adaptive scheduler discrete wavelet trans-form.

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Wheat ear growth modeling based on a polygon

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Frontiers of information Technology & Electronic engineering 2019年第9期20卷 1175-1184页

作者： Jun-xiao XUE Chen-yang SUN Jun-jin CHENG Ming-liang XU Ya-fei LI Shui YU school of software Zhengzhou UniversityZhengzhou 450000China school of information engineering Zhengzhou UniversityZhengzhou 450000China school of software University of Technology SydneySydney 2007Australia

Visual inspection of wheat growth has been a useful tool for understanding and implementing agricultural techniques and a way to accurately predict the growth status of wheat yields for economists and policy decision makers.In this paper,we present a polygonal approach for modeling the growth process of wheat ears.The grain,lemma,and palea of wheat ears are represented as editable polygonal models,which can be re-polygonized to detect collision during the growth process.We then rotate and move the colliding grain to resolve the collision problem.A linear interpolation and a spherical interpolation are developed to simulate the growth of wheat grain,performed in the process of heading and growth of wheat grain.Experimental results show that the method has a good modeling effect and can realize the modeling of wheat ears at different growth stages.

关键词： Visual inspection Virtual crop Three-dimensional modeling

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A Two-Stage Service Replica Strategy for Business Process Efficiency Optimization in Community Cloud

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Chinese Journal of Electronics 2017年第1期26卷 80-87页

作者： LUO Haoyu LIU Jin LIU Xiao State Key Laboratory of software engineering School of Computer Wuhan University school of information Technology Deakin University

Business process that facilitates the organization cooperation and resource sharing plays an important role in Community cloud(Comc). The nature of applications and decentralized infrastructure in Comc make it a big challenge in ensuring the execution efficiency of business process, especially when the business requests are in high concurrency. We present a two-stage service replica strategy to improve the execution efficiency of business process by shortening the response time of request to single service and reducing interaction time among distributed services. We initially utilize the social network property of services to pre-allocate the replicas of key services. After that, queueing model is adopted to determine the required additional replicas according to the quantity and frequency of user requests, then schedule service replicas and requests dynamically. Experiments demonstrate the effectiveness of the proposed strategy.

关键词： Community cloud(Comc) Business process Execution efficiency Service replica Queueing theory

Multi-Level Parallel Network for Brain Tumor Segmentation

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Computer Modeling in engineering & Sciences 2024年第4期139卷 741-757页

作者： Juhong Tie Hui Peng school of software engineering Chengdu University of Information TechnologyChengdu610225China

Accurate automatic segmentation of gliomas in various sub-regions,including peritumoral edema,necrotic core,and enhancing and non-enhancing tumor core from 3D multimodal MRI images,is challenging because of its highly heterogeneous appearance and shape.Deep convolution neural networks(CNNs)have recently improved glioma segmentation performance.However,extensive down-sampling such as pooling or stridden convolution in CNNs significantly decreases the initial image resolution,resulting in the loss of accurate spatial and object parts information,especially information on the small sub-region tumors,affecting segmentation performance.Hence,this paper proposes a novel multi-level parallel network comprising three different level parallel subnetworks to fully use low-level,mid-level,and high-level information and improve the performance of brain tumor segmentation.We also introduce the Combo loss function to address input class imbalance and false positives and negatives imbalance in deep learning.The proposed method is trained and validated on the BraTS 2020 training and validation dataset.On the validation dataset,ourmethod achieved a mean Dice score of 0.907,0.830,and 0.787 for the whole tumor,tumor core,and enhancing tumor core,respectively.Compared with state-of-the-art methods,the multi-level parallel network has achieved competitive results on the validation dataset.

关键词： Convolution neural network brain tumor segmentation parallel network

OSFPMiner: An Optimal Weighted Traversal software Patterns Miner Based on Complex Network

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Chinese Journal of Electronics 2020年第2期29卷 255-264页

作者： HE Haitao SHAN Chun HE Hongdou ZHAO Guyu ZHANG Yangsen TIAN Xiangmin College of information Science and engineering Yanshan University Beijing Key Laboratory of software Security engineering Technique Beijing Institute of Technology school of information Management Beijing Information Science & Technology University

The weighted traversal pattern is important in software system for a better understanding of the internal structure and behavior of software. To mine important patterns of software, a complex network-based Optimal software Fault Patterns Miner is presented. By analyzing the multiple execution traces of software and the relations among functions, we establish the Weighted software Execution Dependency Graph model ultimately.The traversal database is generated through depth-first search strategy and the extraction of software path traversals. According to the downward-closure property,a pruning strategy is adopted by Weighted Frequent Candidate Pattern Tree to cut off more unpromising patterns in advance. A set of important patterns is derived without repeated calculation. The experimental results show that the proposed approach has good performance in the number of weighted frequent candidate patterns and time efficiency.

关键词： Complex network Weighted traversal pattern Upper-bound Pruning

A novel sea-land segmentation based on integral image reconstruction in MWIR images

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Science China(information Sciences) 2017年第6期60卷 269-271页

作者： Yin ZHUANG Dechun GUO He CHEN Fukun BI Long MA Nouman Q.SOOMRO school of information and Electronics Beijing Institute of Technology school of Electronic information engineering North China University of Technology Department of software engineering Mehran University of Engineering and Technology

Dear editor,Middle wave infrared remote(MWIR)technology is the main approach in remote sensing applications,because thermal infrared can work all-time.The MWIR technology can provide valid object information in remote... 详细信息

关键词： In GFM A novel sea-land segmentation based on integral image reconstruction in MWIR images

Resource Load Prediction of Internet of Vehicles Mobile Cloud Computing

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Computers, Materials & Continua 2022年第10期73卷 165-180页

作者： Wenbin Bi Fang Yu Ning Cao Russell Higgs school of Computer and software Dalian Neusoft University of InformationDalian116023China school of information engineering Qingdao Bin Hai UniversityQingdaoChina school of Internet of Things and software Technology Wuxi Vocational College of Science and TechnologyWuxi214028China school of Mathematics and Statistics University College DublinDublinIreland

Load-time series data in mobile cloud computing of Internet of Vehicles(IoV)usually have linear and nonlinear composite characteristics.In order to accurately describe the dynamic change trend of such loads,this study designs a load prediction method by using the resource scheduling model for mobile cloud computing of IoV.Firstly,a chaotic analysis algorithm is implemented to process the load-time series,while some learning samples of load prediction are constructed.Secondly,a support vector machine(SVM)is used to establish a load prediction model,and an improved artificial bee colony(IABC)function is designed to enhance the learning ability of the SVM.Finally,a CloudSim simulation platform is created to select the perminute CPU load history data in the mobile cloud computing system,which is composed of 50 vehicles as the data set;and a comparison experiment is conducted by using a grey model,a back propagation neural network,a radial basis function(RBF)neural network and a RBF kernel function of SVM.As shown in the experimental results,the prediction accuracy of the method proposed in this study is significantly higher than other models,with a significantly reduced real-time prediction error for resource loading in mobile cloud environments.Compared with single-prediction models,the prediction method proposed can build up multidimensional time series in capturing complex load time series,fit and describe the load change trends,approximate the load time variability more precisely,and deliver strong generalization ability to load prediction models for mobile cloud computing resources.

关键词： Internet of Vehicles mobile cloud computing resource load predicting multi distributed resource computing scheduling chaos analysis algorithm improved artificial bee colony function

HTDcr:a job execution framework for high-throughput computing on supercomputers

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Science China(information Sciences) 2024年第1期67卷 69-85页

作者： Jiazhi JIANG Dan HUANG Hu CHEN Yutong LU Xiangke LIAO school of Computer Science and engineering Sun Yat-sen University school of software engineering South China University of Technology

High-throughput computing(HTC) is a computing paradigm that aims to accomplish jobs by easily breaking them into smaller,independent components.However,it requires a large amount of computing power for a long time.Most existing HTC frameworks are job-oriented without support for coscheduling with hardware architecture and task-level execution.Also,most of the frameworks reach a limited scale,and their usability needs further improvement.Herein,we present HTDcr,a job execution framework for the HTC on supercomputers.This study aims to improve the throughput,task dispatching,and usability of the framework.In detail,the throughput optimizations include a sophisticated designed task management system,a hierarchical scheduler,and the co-optimization of the task-scheduling strategy with the application and hardware characteristics.The optimizations for usability include a programable execution workflow,mechanisms for more robust and reliable service qualities,and a fine-grained resource allocation system for the colocation of multiple jobs.According to our evaluations,HTDcr can achieve outstanding scalability and high throughput on large-scale clusters for the HTC workload.We evaluate HTDcr with several microbenchmarks and real-world applications on Tianhe-2 and Sunway TaihuLight to demonstrate its effects on existing design mechanisms.For instance,the task scheduling for two real-world applications integrated with the application and hardware characteristics achieves 1.7× and 1.9× speedups over the basic task-scheduling strategy.

关键词： high-throughput computing supercomputer task scheduling middleware password guessing

Deep Contextual Representation Learning for Identifying Essential Proteins via Integrating Multisource Protein Features

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Chinese Journal of Electronics 2023年第4期32卷 868-881页

作者： LI Weihua LIU Wenyang GUO Yanbu WANG Bingyi QING Hua school of information Science and engineering Yunnan University College of software engineering Zhengzhou University of Light Industry Institute of Highland Forest Science Chinese Academy of Forestry

Essential proteins with biological functions are necessary for the survival of organisms. Computational recognition methods of essential proteins can reduce the workload and provide candidate proteins for biologists. However, existing methods fail to efficiently identify essential proteins, and generally do not fully use amino acid sequence information to improve the performance of essential protein recognition. In this work, we propose an end-to-end deep contextual representation learning framework called DeepIEP to automatically learn biological discriminative features without prior knowledge based on protein network heterogeneous information. Specifically, the model attaches amino acid sequences as the attributes of each protein node in the protein interaction network, and then automatically learns topological features from protein interaction networks by graph embedding algorithms. Next, multi-scale convolutions and gated recurrent unit networks are used to extract contextual features from gene expression profiles. The extensive experiments confirm that our DeepIEP is an effective and efficient feature learning framework for identifying essential proteins and contextual features of protein sequences can improve the recognition performance of essential proteins.

关键词： Essential proteins Protein interaction networks Gene expression profile Deep neural networks