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<title cf:type="text"><![CDATA[Editorial department of the Journal of National University of Defense Technology -->专栏：高性能计算]]></title>
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<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Prediction method of port blocking failure in high performance interconnection networks]]></title>
<link><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202205001]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[With the increase of system scale, chip power consumption and link rate, the overall failure rate of high-performance interconnection networks will continue rising, and the traditional operation and maintenance methods will be difficult to sustain, which brings great challenges to the overall reliability and availability of HPC(high performance computing). An unsupervised algorithm prediction model for serious network failures such as network port blocking was proposed. In this model, the symptomatic rules were extracted from the history information of the switch port status register and a new feature vector was formed. The K-means clustering algorithm was used to learn and classify the feature vectors. In the prediction, the DES(double exponential smoothing) algorithm was used to predict the port state in the future through a combination of the current state of the port, and a new feature vector was obtained and K-means algorithm was used to predict whether the port blocking failure would occur. The topology information was used to build independent sub prediction models with ToR switch ports and Spine switch ports respectively, so as to further improve the accuracy of prediction. The experimental results show that the prediction model can maintain the recall rate of 88.2%, and reach the accuracy rate of 65.2%. It can provide effective early warning and guidance for the operation and maintenance personnel in the actual system.]]></description>
<pubDate>2022/9/28 0:00:00</pubDate>
<category><![CDATA[专栏：高性能计算]]></category>
<author><![CDATA[XU Jiaqing, HU Xiaotao, YANG Hanzhi, WANG Qiang, ZHANG Lei, TANG Fuqiao]]></author>
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<atom:name>XU Jiaqing, HU Xiaotao, YANG Hanzhi, WANG Qiang, ZHANG Lei, TANG Fuqiao</atom:name>
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<guid><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202205001]]></guid><cfi:id>13</cfi:id><cfi:read>true</cfi:read></item>
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<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Predicting the job running time with job name hierarchical clustering algorithm]]></title>
<link><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202205002]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[Predicting the job running time is beneficial to improve the scheduling performance of the system, and the clustering can help to train better prediction models. Traditional clustering algorithms are difficult to cluster similar job names. In order to better cluster similar jobs, the job name hierarchical clustering algorithm of letter-structure-number was constructed by analyzing the semantic importance of their components. Taking the real data of two supercomputers as an example, the data clustered by this algorithm was used to train the model. The experimental results show that the prediction accuracy of the model is better than that of the traditional method, and the overall prediction accuracy is 70%～80%.]]></description>
<pubDate>2022/9/28 9:42:10</pubDate>
<category><![CDATA[专栏：高性能计算]]></category>
<author><![CDATA[ZHOU Longfang, YANG Wenxiang, HAN Yongguo, ZHANG Xiaorong, YU Jie, FENG Jinghua, ZHANG Jian, LI Yuqi, XIAN Gang, WU Yadong, WANG Guijuan]]></author>
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<atom:name>ZHOU Longfang, YANG Wenxiang, HAN Yongguo, ZHANG Xiaorong, YU Jie, FENG Jinghua, ZHANG Jian, LI Yuqi, XIAN Gang, WU Yadong, WANG Guijuan</atom:name>
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<guid><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202205002]]></guid><cfi:id>12</cfi:id><cfi:read>true</cfi:read></item>
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<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Optimizations of graph coloring method for unstructured finite volume computational fluid dynamics on GPU]]></title>
<link><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202205003]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[Graph coloring was used to address resource competition for the two typical computing procedures, including the flux summation and the calculation of local maximum pressure. There were three optimizations applied on graph coloring including shared memory, the reordering of volume and face indices, and the reordering of face variables. The 3D aerodynamics application with a series of mesh sizes was used in the performance test by double and single precision floating point operations on GPU Nvidia Tesla V100 and K80. The performance tests show that the shared memory is not obvious in performance. Furthermore, the reorder of volume and face indices reduces the performance of graph coloring.It is found that the reorder of face variables can increase performance remarkably. Specifically, the performance of graph coloring is increased by around 20% on V100 and 15% on K80.]]></description>
<pubDate>2022/9/28 9:42:10</pubDate>
<category><![CDATA[专栏：高性能计算]]></category>
<author><![CDATA[ZHANG Xi, SUN Xu, GUO Xiaohu, DU Yunfei, LU Yutong, LIU Yang]]></author>
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<atom:name>ZHANG Xi, SUN Xu, GUO Xiaohu, DU Yunfei, LU Yutong, LIU Yang</atom:name>
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<guid><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202205003]]></guid><cfi:id>11</cfi:id><cfi:read>true</cfi:read></item>
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<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Graphics processing unit resource management for computational fluid dynamics]]></title>
<link><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202205004]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[Aiming at the problem of low resource utilization of GPU (graphics processing unit) in the process of solving CFD (computational fluid dynamics), a CFD-oriented GPU resource optimization management scheme was proposed. Based on the characterization of the CFD and tasks running concurrently, a reasonable scheduling scheme was designed. By dynamically changing the startup scale and time of different tasks, our method was able to reduce resource competition while improving the effective use of GPU resources. The experimental results show that compared with the baseline method, the average speedup ratio of our proposed resource management scheme reaches 1.64x speedup under different task scales, and the use of GPU hardware resources has also been significantly improved.]]></description>
<pubDate>2022/9/28 9:42:11</pubDate>
<category><![CDATA[专栏：高性能计算]]></category>
<author><![CDATA[WENG Yue, ZHANG Xianwei, ZHANG Xi, LU Yutong]]></author>
<atom:author xmlns:atom="http://www.w3.org/2005/Atom">
<atom:name>WENG Yue, ZHANG Xianwei, ZHANG Xi, LU Yutong</atom:name>
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<guid><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202205004]]></guid><cfi:id>10</cfi:id><cfi:read>true</cfi:read></item>
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<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Identifying causes of execution failure for parallel programs]]></title>
<link><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202205005]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[With the increasing of scale and complexity of high-performance computing systems, the mean time between failures is getting shorter, which causes an increasing probability of execution-failure caused by the hardware and software failures for parallel programs. In addition, the possible programming errors (i.e. bugs) that exist in parallel programs can also lead to execution failure. Approaches to deal with the above two types of execution failures are totally different, therefore, when an execution-failure occurs, the programmer must figure out if the failure is caused by a system fault or a programming bug. In response to this issue, a system to identifying causes of execution-failures for parallel programs was designed and implemented on the basis of the Slurm. The system has all the supported features of Slurm, as well as the ability to monitor job status, re-submit and re-run jobs. The experimental results of the job execution show that the system can distinguish the type of program execution-failures. Experiments conducted with fault injection also demonstrates the accuracy of the system.]]></description>
<pubDate>2022/9/28 9:42:11</pubDate>
<category><![CDATA[专栏：高性能计算]]></category>
<author><![CDATA[LIU Yi, GAO Yulin, ZHANG Guozhen]]></author>
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<atom:name>LIU Yi, GAO Yulin, ZHANG Guozhen</atom:name>
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<guid><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202205005]]></guid><cfi:id>9</cfi:id><cfi:read>true</cfi:read></item>
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<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Synchrotron radiation source image compression method based on difference and neural network]]></title>
<link><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202205006]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[For the common image lossless compression methods cannot work well. Thus, a lossless compression method for synchrotron radiation source images based on image difference and neural network was proposed. The image difference method was used to reduce the linear correlations among images. The neural network was trained to learn the nonlinear correlations in the images sequence, and the pixel value was compressed with arithmetic coding using the predicted distribution. To reduce the predicting time and coding time, the pixel value was splitted into two parts for parallel compression. The tests based on the images of Shanghai Synchrotron Radiation Facility show that the proposed method can improve more than 20% in compression ratio compared to PNG(portable network graphics), JPEG2000, FLIF(free lossless image format), and the pixel value split can reduce 30% of the time in predicting and coding.]]></description>
<pubDate>2022/9/28 9:42:11</pubDate>
<category><![CDATA[专栏：高性能计算]]></category>
<author><![CDATA[FU Shiyuan, WANG Lu, CHENG Yaodong, CHEN Gang]]></author>
<atom:author xmlns:atom="http://www.w3.org/2005/Atom">
<atom:name>FU Shiyuan, WANG Lu, CHENG Yaodong, CHEN Gang</atom:name>
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<guid><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202205006]]></guid><cfi:id>8</cfi:id><cfi:read>true</cfi:read></item>
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<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Parallel job characteristic analysis toolkit based on job accounting logs:JobCAT]]></title>
<link><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202205007]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[Characteristic analysis of parallel job is the basics of workload analysis research. Job accounting log is an important data source of job characteristic analysis, however, existing tools cannot do statistics analysis with application name, because of application name not recorded in job accounting log. To solve this problem, a novel marking method for job accounting log was proposed, which based on keyword fuzzy matching. Combined with a general job data model and a flexible extensible software architecture, a parallel job feature analysis tool JobCAT was implemented. According to verification test by millions of job accounting log data from a supercomputer system, the log marking rate of JobCAT was greater than 95%. JobCAT supports 7 plugins and 29 statistical reports, and can easily make analysis report classified by application name, which has practical value to workload analysis research.]]></description>
<pubDate>2022/9/28 9:42:11</pubDate>
<category><![CDATA[专栏：高性能计算]]></category>
<author><![CDATA[TIAN Hongyun, LIU Xu, WU Linping, LUO Hongbing, MO Zeyao]]></author>
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<atom:name>TIAN Hongyun, LIU Xu, WU Linping, LUO Hongbing, MO Zeyao</atom:name>
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<guid><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202205007]]></guid><cfi:id>7</cfi:id><cfi:read>true</cfi:read></item>
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<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Prediction algorithm for failed batch jobs in co-located cloud]]></title>
<link><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202205008]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[In order to reduce the risk of failed batch jobs in co-located cloud, the K-means algorithm was used to divide batch jobs into four categories.On the basis of classification, the TLNM (two-layer nested classification model) was proposed and the prediction algorithm based on TLNM was implemented. Experiment results based on Ali Trace 2018 data set show that the ROC(receiver operating characteristic) curve of this algorithm is significantly better than other commonly used classifiers, and the area under the ROC curve (i.e.AUC) can reach 0.978, indicating that this algorithm has good classification performance. At the same time, the recall rate can reach 0.951. Through the confusion matrix, it can be seen that the TLNM algorithm can accurately predict the failed batch jobs.]]></description>
<pubDate>2022/9/28 9:42:11</pubDate>
<category><![CDATA[专栏：高性能计算]]></category>
<author><![CDATA[LIN Weiwei, SHI Fang, LI Yurui, LIU Fagui, LIU Jie, PENG Shaoliang, WANG James Z.]]></author>
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<atom:name>LIN Weiwei, SHI Fang, LI Yurui, LIU Fagui, LIU Jie, PENG Shaoliang, WANG James Z.</atom:name>
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<guid><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202205008]]></guid><cfi:id>6</cfi:id><cfi:read>true</cfi:read></item>
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<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Accelerating parallel reduction and scan primitives on ReRAM-based architectures]]></title>
<link><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202205009]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[Reduction and scan are two critical primitives in parallel computing. Thus, accelerating reduction and scan shows great importance. However, the Von Neumann architecture suffers from performance and energy bottlenecks known as “memory wall” due to the unavoidable data migration. Recently, NVM (non-volatile memory) such as ReRAM (resistive random access memory), enables in-situ computing without data movement and its crossbar architecture can perform parallel GEMV (matrix-vector multiplication) operation naturally in one step. ReRAM-based architecture has demonstrated great success in many areas, e.g. accelerating machine learning and graph computing applications, etc. Parallel acceleration methods were proposed for reduction and scan primitives on ReRAM-based PIM(processing in memory) architecture, the computing process in terms of GEMV and the mapping method on the ReRAM crossbar were focused, and the co-design of software and hardware was realized to reduce power consumption and improve performance. Compared with GPU, the proposed reduction and scan algorithm achieved substantial speedup by two orders of magnitude, and the average acceleration ratio can also reach two orders of magnitude. The case of segmentation can achieve up to five (four on average) orders of magnitude. Meanwhile, the power consumption decreased by 79%.]]></description>
<pubDate>2022/9/28 9:42:11</pubDate>
<category><![CDATA[专栏：高性能计算]]></category>
<author><![CDATA[JIN Zhou, DUAN Yiru, YI Enxin, JI Haonan, LIU Weifeng]]></author>
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<atom:name>JIN Zhou, DUAN Yiru, YI Enxin, JI Haonan, LIU Weifeng</atom:name>
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<guid><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202205009]]></guid><cfi:id>5</cfi:id><cfi:read>true</cfi:read></item>
<item>
<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Detection and optimization approaches for synchronization bottlenecks in parallel programs]]></title>
<link><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202205010]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[Aiming at the problem that improper locks in parallel programs may lead to performance bottlenecks, an approach called IdeSync was proposed to detect and optimize synchronization bottlenecks. IdeSync leveraged the static analysis to obtain the synchronized methods and blocks, and constructed a static synchronization dependency graph. The dynamic analysis technology based on the execution path was used to analyze the synchronization dependency and build the synchronization dependency graph.In order to expose the performance bottleneck, the performance change of the critical section was monitored by increasing the program workload on the synchronization dependency graph, and optimization suggestions were given for the detected synchronization bottleneck. The effectiveness of IdeSync was evaluated with 12 large real-world applications such as HSQLDB, SPECjbb2005 and RxJava, and a total of 72 synchronization bottlenecks were detected. All these bottlenecks were optimized based on IdeSync′s suggestion to achieve performance improvements, which shows that IdeSync can effectively detect and optimize synchronization bottlenecks.]]></description>
<pubDate>2022/9/28 0:00:00</pubDate>
<category><![CDATA[专栏：高性能计算]]></category>
<author><![CDATA[ZHANG Yang, LI Liuxu]]></author>
<atom:author xmlns:atom="http://www.w3.org/2005/Atom">
<atom:name>ZHANG Yang, LI Liuxu</atom:name>
</atom:author>
<guid><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202205010]]></guid><cfi:id>4</cfi:id><cfi:read>true</cfi:read></item>
<item>
<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Node priority optimization in distributed heterogeneous clusters]]></title>
<link><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202205011]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[Node priority is often used to evaluate the performance of heterogeneous cluster nodes, and it is of great importance to provide suitable weight for each priority evaluation index. The AHP (analytic hierarchy process) was chosen to establish the evaluation index system of node priority, and the initial weight of each index was calculated. The BP (back propagation) neural network was then used to optimize the weights obtained by using AHP. The input of the BP neural network was the node′s performance index values collected during execution of cluster, and the output was the corresponding priority of the node. After the network training, the weight matrix was obtained and used to calculate the optimized weights. The experimental results show that the cluster node priority evaluation model based on AHP and BP can evaluate the node performance more accurately. Compared with the default resource allocation algorithm of Spark and the comparison algorithm with unoptimized weights, the cluster performance is improved effectively by using the node priority optimized. When running the same kind of load with different amount of data, the average cluster performance increases by 16.64% and 9.76%, respectively; and when running different loads with the same amount of data, the average performance of the cluster increases by 12.49% and 6.54%, respectively.]]></description>
<pubDate>2022/9/28 9:42:11</pubDate>
<category><![CDATA[专栏：高性能计算]]></category>
<author><![CDATA[HU Yahong, QIU Yuanyuan, MAO Jiafa]]></author>
<atom:author xmlns:atom="http://www.w3.org/2005/Atom">
<atom:name>HU Yahong, QIU Yuanyuan, MAO Jiafa</atom:name>
</atom:author>
<guid><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202205011]]></guid><cfi:id>3</cfi:id><cfi:read>true</cfi:read></item>
<item>
<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Hardware counter multiplexing estimation algorithm using deep learning]]></title>
<link><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202205012]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[A state-of-art deep learning method was proposed to achieve higher accuracy of MPX(multiplexing) estimation. By analyzing the similarity between the MPX results and the real data, it was proved that hardware counts gained by running the same program was linear correlated. By applying the MLP(multilayer perceptron) and Bi-GRU(bidirectional gated recurrent unit) model, the MPX data was fitted. Based on DTW (dynamic time warping), a new metric DTW-cost was proposed to judge the accuracy of MPX result. Experiment results show that when sampling 15 hardware events simultaneously, average result of 13 high performance computing applications gained by the MLP model has a 10.53% higher relative accuracy than the fixed interpolation method. The MLP model has a 19.8% improvement at most. On the hardware events which MLP has a relatively poor performance, the Bi-GRU model improved relative accuracy score by 28.8% on average.]]></description>
<pubDate>2022/9/28 9:42:11</pubDate>
<category><![CDATA[专栏：高性能计算]]></category>
<author><![CDATA[WANG Yichao, WANG Liuzhen, LIN Xinhua]]></author>
<atom:author xmlns:atom="http://www.w3.org/2005/Atom">
<atom:name>WANG Yichao, WANG Liuzhen, LIN Xinhua</atom:name>
</atom:author>
<guid><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202205012]]></guid><cfi:id>2</cfi:id><cfi:read>true</cfi:read></item>
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<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Software crowdsourcing tasks assignment supporting fuzzy measurement of workers′ qualification and role collaboration]]></title>
<link><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202205013]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[The existing researches on crowdsourcing task assignment don′t involve in measuring the uncertainty of workers′ qualification, and don′t achieve the collaborative assignment in many-to-many mode between tasks and workers from the angle of crowdsourcing platform. Thus, an assignment approach of software crowdsourcing tasks was proposed supporting the fuzzy measurement of workers qualification and role collaboration. Integrating the past performance of workers and the expectation of tasks′ demands, this approach employed fuzzy interval numbers to evaluate the multiple attributes ability matching degree, and aggregated the comprehensive qualification via the fuzzy analytic hierarchy process method. By introducing the role-based collaboration theory, the many-to-many software crowdsourcing tasks assignment was formulated as a combinatorial optimization problem related to a task set and a worker group, and the constraints, including the tasks′ weights, quantity of workers and potential conflicts, were used to enhance the efficiency and success rate of tasks assignment. A solution based on the CPLEX package was presented to solve the problem. Simulation experiments show that this method can efficiently and accurately realize the collaborative allocation of crowdsourcing tasks under the premise of ensuring the best completion quality of global tasks.]]></description>
<pubDate>2022/9/28 9:42:11</pubDate>
<category><![CDATA[专栏：高性能计算]]></category>
<author><![CDATA[MA Hua, CHEN Yuepeng, HUANG Zhuoxuan, TANG Wensheng, LOU Xiaoping]]></author>
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<atom:name>MA Hua, CHEN Yuepeng, HUANG Zhuoxuan, TANG Wensheng, LOU Xiaoping</atom:name>
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