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A Hybrid Approach to parallel Pattern Discovery in C++

A Hybrid Approach to Parallel Pattern Discovery in C++

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euromicro conference on parallel, distributed and network-based processing

作者： C. Brown V. Janjic A. Barwell J. Thomson R. Castañeda Lozano M. Cole B. Franke J.D. Garcia-Sanchez D. Del Rio Astorga K. MacKenzie School of Computer Science University of St Andrews St Andrews UK School of Informatics University of Edinburgh Edinburgh UK Computer Science and Engineering Dept. Universidad Carlos III de Madrid Madrid Spain IOHK

ISBN: (数字)9781728165820

ISBN: (纸本)9781728165837

parallel pattern libraries offer a strong combination of abstraction and performance. However, discovering places in sequential code where parallel patterns should be introduced is still highly non-trivial, often requiring expert manual analysis and profiling. We present a hybrid discovery technique to detect instances of parallel patterns in sequential code. This employs both static and dynamic trace-based analysis, together with hotspot detection. We evaluate our pattern discovery mechanism on a number of representative benchmarks. We evaluate the performance of the resulting parallelised benchmarks on a 24-core parallel machine.

关键词： Pattern matching Benchmark testing parallel processing Detectors Shape C++ languages Instruments

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A Hybrid Model for QoS Prediction based on Improved Conditional Variational Autoencoder

A Hybrid Model for QoS Prediction based on Improved Conditio...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Mengwei Wu Qin Lu Yingxue Wang Yichao Wang Huanyu Chen Weixiao Li School of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China School of Information and Automation Engineering Qilu University of Technology (Shandong Academy of Sciences) Jinan China Information Technology Center China Mobile Communication Group Co. Ltd Beijing China

As a nonfunctional attribute of Web services, Quality of Service (QoS) is a crucial criterion that researchers utilize to develop personalized service recommendation. The QoS history is thin and contains noisy data because of the network environment and other inevitable factors. In order to increase the effectiveness of Web service recommendation, it is essential to accurately predict the missing QoS values. Therefore, a model for QoS prediction based on improved Conditional Variational Autoencoder is proposed in order to improve the prediction accuracy of missing QoS. The Bias based Singular Value Decomposition (BiasSVD) is employed by the model to pre-populate the initial sparse QoS matrix with missing values. By improved Conditional Variational Autoencoder, the pre-filled matrix is learned and the QoS is reconstructed. Then, the users' plausible similar neighbors are filtered using the Pearson Correlation Coefficient and Independent Sample T-test. The missing QoS is predicted by combining the reconstruction matrix. Last but not least, the experimental results on the actual dataset WSDream demonstrate that the model improves the QoS prediction accuracy to considerable degree.

关键词： Correlation coefficient Web services Quality of service Predictive models Sparse matrices Matrix decomposition Noise measurement

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Lightweight Mobile Devices Indoor Location based on Image Database 26

Lightweight Mobile Devices Indoor Location Based on Image Da...

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26th IEEE International conference on parallel and distributed Systems (IEEE ICPADS)

作者： Gao, Ran Zhao, Yanchao Tang, Maoxing Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Peoples R China Collaborat Innovat Ctr Novel Software Technol & I Nanjing Peoples R China

ISBN: (纸本)9781728190747

Among the numerous indoor localization technologies, image-based solution has great advantages on convenient access from smartphone and its infrastructure-less deployment. However, the image-based localization also suffers from two key disadvantages, which hinders the universal application. Firstly, it requires a large amount of computing and storage resources, which is difficult to achieve for the mobile device, while cloudbased scheme incurs unacceptable delay. Secondly, although this solution doesn't require infrastructure, it still suffers from labor intensive image-database construction and updates. To overcome these limitations, we propose an image-based indoor localization method featured with realtime localization and labor-less image database update. This method mainly innovates in two aspects. First, we propose a mobile device compatible image database compressing framework, which enable realtime and accurate ondevice image searching even in a large scenario. Our localization method achieves resource efficiency (in terms of storage and processing) by only keeping image feature vectors, and employing the efficient k-mean tree to search for the best matched image. Secondly, to achieve labor-less image database updating, we mainly add high-quality and informative query image into the database. These query image could compensate the missing information or changed scenario in a up-to-date manner. We conduct real experiments in Android Platform to verify the feasibility and performance of the localization method. Experiment results show that our method has good accuracy (90% location errors are within 1.5m) and high real-time performance (average location delay is less than 0.5s).

关键词： Image-based localization real-time smartphone-based localization indoor location image database update

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distributed processing of k Shortest Path Queries over Dynamic Road networks 20

Distributed Processing of k Shortest Path Queries over Dynam...

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ACM SIGMOD International conference on Management of Data (SIGMOD)

作者： Yu, Ziqiang Yu, Xiaohui Koudas, Nick Liu, Yang Li, Yifan Chen, Yueting Yang, Dingyu Yantai Univ Yantai Peoples R China York Univ N York ON Canada Univ Toronto Toronto ON Canada Wilfrid Laurier Univ Waterloo ON Canada Alibaba Grp Hangzhou Peoples R China MNR Key Lab Urban Land Resources Monitoring & Simulat Shenzhen Peoples R China

ISBN: (纸本)9781450367356

The problem of identifying the k-shortest paths (KSPs for short) in a dynamic road network is essential to many location-based services. Road networks are dynamic in the sense that the weights of the edges in the corresponding graph constantly change over time, representing evolving traffic conditions. Very often such services have to process numerous KSP queries over large road networks at the same time, thus there is a pressing need to identify distributed solutions for this problem. However, most existing approaches are designed to identify KSPs on a static graph in a sequential manner (i.e., the (i + 1)th shortest path is generated based on the ith shortest path), restricting their scalability and applicability in a distributed setting. We therefore propose KSP-DG, a distributed algorithm for identifying k-shortest paths in a dynamic graph. It is based on partitioning the entire graph into smaller subgraphs, and reduces the problem of determining KSPs into the computation of partial KSPs in relevant subgraphs, which can execute in parallel on a cluster of servers. A distributed two-level index called DTLP is developed to facilitate the efficient identification of relevant subgraphs. A salient feature of DTLP is that it indexes a set of virtual paths that are insensitive to varying traffic conditions, leading to very low maintenance cost in dynamic road networks. This is the first treatment of the problem of processing KSP queries over dynamic road networks. Extensive experiments conducted on real road networks confirm the superiority of our proposal over baseline methods.

关键词： dynamic graph k-shortest paths distributed computing

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Cooperative Caching Strategy based on Dynamic Cache Value Perception in Edge networks

Cooperative Caching Strategy Based on Dynamic Cache Value Pe...

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IEEE International Symposium on parallel and distributed processing with Applications and IEEE International conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Yue Guo Guangxu Zhou Junfeng Hou Hongjun Zhang Yaofeng Li Xuchang Branch of Henan Provincial Tobacco Company Xuchang

Caching the content that users will request at the edge of the network can effectively alleviate the pressure of the core network, but the existing research results pay less attention to the cache value of the content, which makes the cache strategy performance low. A dynamic cache value aware collaborative cache strategy is proposed, which is different from the traditional single mode. The user layer caches the content according to the user’s request intention, and the MBS and SBS layers consider the global information for collaborative cache placement. Combined with the user’s movement trajectory, residence time and user’s request intention, a value perception method of dynamic content cache is proposed to determine the content to be cached. A content cache decision algorithm based on deep reinforcement learning is also designed, which uses the cooperative cache placement decision with MBS and SBS layers. Simulation results show that the cache hit rate of the proposed cache strategy is increased by 5.0% 10.8% and 18.9% on average compared with the three baseline schemes, indicating that the proposed strategy can significantly offload core network traffic and improve edge cache performance.

关键词： Deep learning Cooperative caching Simulation Heuristic algorithms Collaboration Telecommunication traffic Reinforcement learning

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CoopCL: Cooperative Execution of OpenCL Programs on Heterogeneous CPU-GPU Platforms

CoopCL: Cooperative Execution of OpenCL Programs on Heteroge...

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euromicro conference on parallel, distributed and network-based processing

作者： Konrad Moreń Diana Göhringer Department Object Recognition Fraunhofer IOSB Gutleuthausstr. 1 Ettlingen Germany Adaptive Dynamic Systems TU Dresden Dresden Germany

ISBN: (数字)9781728165820

ISBN: (纸本)9781728165837

In this work, we present CoopCL, an C++ API and runtime that abstracts and unifies the cooperative workload execution on multi-core CPU and GPU. The CoopCL takes a OpenCL-C kernel function and automatically uses both the CPU and the GPU to execute it in parallel. We propose a method that transparently manages the data transfers and synchronization necessary to ensure a coherence without requiring any effort from the programmer. The CoopCL is completely portable across different machines including platforms with discrete and integrated GPUs. Across a set of diverse benchmarks, our runtime shows a mean speedup of nearly 10% over a GPU only execution and 30% over a multi-core CPU. We also achieve up to a 52% reduction in energy consumption compared to the CPU only execution.

关键词： Task analysis Graphics processing units Kernel Runtime Central processing Unit Synchronization Hardware

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AccDP: Accelerated Data-parallel distributed DNN Training for Modern GPU-based HPC Clusters

AccDP: Accelerated Data-Parallel Distributed DNN Training fo...

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International conference on High Performance Computing

作者： Nawras Alnaasan Arpan Jain Aamir Shafi Hari Subramoni Dhabaleswar K Panda Department of Computer Science and Engineering The Ohio State University Columbus Ohio USA

Deep Learning (DL) has become a prominent machine learning technique due to the availability of efficient computational resources in the form of Graphics processing Units (GPUs), large-scale datasets and a variety of models. The newer generation of GPUs are being designed with special emphasis on optimizing performance for DL applications. Also, the availability of easy-to-use DL frameworks—like PyTorch and TensorFlow— has enhanced productivity of domain experts to work on their custom DL applications from diverse domains. However, existing Deep Neural network (DNN) training approaches may not fully utilize the newly emerging powerful GPUs like the NVIDIA A100—this is the primary issue that we address in this paper. Our motivating analyses show that the GPU utilization on NVIDIA A100 can be as low as 43% using traditional DNN training approaches for small-to-medium DL models and input data size. This paper proposes AccDP—a data-parallel distributed DNN training approach—to accelerate GPU-based DL applications. AccDP exploits the Message Passing Interface (MPI) communication library coupled with the NVIDIA’s Multi-Process Service (MPS) to increase the amount of work assigned to parallel GPUs resulting in higher utilization of compute resources. We evaluate our proposed design on different small-to-medium DL models and input sizes on the state-of-the-art HPC clusters. By injecting more parallelism into DNN training using our approach, the evaluation shows up to 58% improvement in training performance on a single GPU and up to 62% on 16 GPUs compared to regular DNN training. Furthermore, we conduct an in-depth characterization to determine the impact of several DNN training factors and best practices—including the batch size and the number of data loading workers— to optimally utilize GPU devices. To the best of our knowledge, this is the first work that explores the use of MPS and MPI to maximize the utilization of GPUs in distributed DNN training.

关键词： Training Deep learning Productivity Performance evaluation Computational modeling Message passing Neural networks

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Non-Cooperative Game for Edge Server Placement in Wireless Body Area network

Non-Cooperative Game for Edge Server Placement in Wireless B...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Xiaolong Xu Yiming Fei Xiaokang Zhou Xiaoyong Li Yong Cheng Lianyong Qi Wanchun Dou School of Computer Science Nanjing University of Information Science and Technology Nanjing China Faculty of Data Science Shiga University Tokyo Japan College of Meteorology and Oceanography National University of Defense Technology Changsha China College of Computer Science and Technology China University of Petroleum (East China) China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

The outbreak of the coronavirus disease 2019 (COVID-19) has become the worst public health event in the whole world, threatening the physical and mental health of hundreds of millions of people. However, because of the high survivability of the virus, it is impossible for humans to eliminate viruses completely. For this reason, it is particularly important to strengthen the prevention of the transmission of viruses and monitor the physical status of the crowd. Wireless sensors are a key player in the fight against the current global outbreak of the Covid-19 pandemic, where they are playing an important role in monitoring human health. The Wireless Body Area network (WBAN) composed of these wireless sensor devices can monitor human health data without interference for a long time, and update the data in almost real time through the Internet of Things (IoT). However, because the data monitored by the devices is relatively large and the transmission distance is long, only transmitting the data to medical centers through the personal devices (PB) cannot get feedback in time. We propose a non-cooperative game-based server placement method, which is named ESP-19 to improve the efficiency of transmission data of wireless sensors. In this paper, experimental tests are conducted based on the distribution of Shanghai Telecom's base stations, and then the performance of ESP-19 is evaluated. The results show that the proposed method in this paper outperforms the comparison method in terms of service delay.

关键词： Wireless communication COVID-19 Wireless sensor networks Computer viruses Games Body area networks Sensors

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A Global Opinion-influencing Consensus Model based on the DeGroot

A Global Opinion-influencing Consensus Model based on the De...

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IEEE International Symposium on parallel and distributed processing with Applications and IEEE International conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Yuntian Zhang Xiaoliang Chen Zexia Huang Xianyong Li Yajun Du School of Computer and Software Engineering Xihua University Chengdu China College of Science Xihua University Chengdu China

Social networks have become an important platform for transmitting and sharing information due to the rapid development of online social networks. Opinion dynamics have been examined through the consensus-reaching process (CRP) in social networks, which aims to model the evolution of opinions in social networks so that agents can reach a consensus. Many researchers have solved the CRP problem by improving the DeGroot model, which models divide agents into two categories, leaders and followers, and change the network structure or the values of the agents’ opinions to bring the network to a consensus. However, all of them require the construction of leaders who can influence the entire network. Consensus cannot be achieved without the leaders. This paper proposes a global opinion-influencing consensus model (GOCM) to solve the CRP problem. A network agent refers not only to the opinion of its neighbors but also to the average opinion of the entire network. The numerical analysis indicates that our proposed GOCM can significantly accelerate the achievement of an expected consensus.

关键词： Analytical models Social networking (online) Numerical analysis Computational modeling Ubiquitous computing Numerical models

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MapReduce-based BP neural network classification of aquaculture water quality

MapReduce-based BP neural network classification of aquacult...

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2020 International conference on Computer Information and Big Data Applications, CIBDA 2020

作者： Wen, Yan Li, Min Ye, Yu Chengdu Agricultural College Academy of Mechatromcs and Information Chengdu China

ISBN: (纸本)9781728198378

As information technology develops and prevails across the globe, the informatization and data processing efficiency in the aquaculture field are exerting increasing impact on the intelligent conversion of this field. The aquaculture water quality indicators were analyzed by a feedforward error back propagation algorithm (BP neural network) with strong nonlinear mapping capacity, and the complicated nonlinear relations among the parameters of the aquaculture environment were solved. An aquaculture element analysis model was proposed, the Johnson attribute reduction algorithm based on the discernibility matrix was used to optimize the traditional algorithm, and the network convergence speed was increased under a given accuracy. The MapReduece distributed programming model was then used to perform parallel design of the BP neural network algorithm to meet the needs of massive data processing in aquaculture platforms. Also, case studies were performed to analyze the aquaculture element model and the parallel learning algorithm, and the big data framework design and data analysis method are integrated to develop an efficient, fault-tolerant aquaculture data management, mining, visualization big data system. © 2020 IEEE.

关键词： Aquaculture

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