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Application Characterization for Near Memory processing

Application Characterization for Near Memory Processing

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euromicro conference on parallel, distributed and network-based processing

作者： Maryam S. Hosseini Masoumeh Ebrahimi Pooria Yaghini Nader Bagherzadeh Center for Pervasive Communications and Computing (CPCC) University of California Irvine KTH Royal Institute of Technology Sweden

Data movement between memory subsystem and processor unit is a crippling performance and energy bottleneck for data-intensive applications. Near Memory processing (NMP) is a promising solution to alleviate the data movement bottleneck. The introduction of 3D-stacked memories and more importantly hybrid memory systems enable the long-wished NMP capability. This work explores the feasibility and efficacy of having NMP on the hybrid memory system for a given set of applications. In this paper, we first redefine a set of NMP-centric performance metrics in order to analyze the efficacy of a given processing unit. Leveraging the proposed metrics, we characterize various sets of applications to assess the suitability of a processing unit in terms of performance. Specifically, in this work we motivate the efficiency of NMP subsystems to process memory-intensive applications when 3D-NVM technologies are employed.

关键词： Measurement Nonvolatile memory Memory management Performance analysis Central processing Unit

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Enabling Support for Zero Copy Semantics in an Asynchronous Task-based Programming Model 27th

Enabling Support for Zero Copy Semantics in an Asynchronous ...

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27th International European conference on parallel and distributed Computing (Euro-Par)

作者： Bhat, Nitin White, Sam Kale, Laxmikant, V Charmworks Inc Urbana IL 61801 USA Univ Illinois Dept Comp Sci 1304 W Springfield Ave Urbana IL 61801 USA

ISBN: (纸本)9783031061561;9783031061554

Communication is critical to the scalable and efficient performance of scientific simulations on extreme scale computing systems. Part of the promise of task-based programming models is that they can naturally overlap communication with computation and exploit locality between tasks. Copy-based semantics using eager communication protocols easily enable such asynchrony by alleviating the responsibility of buffer management from the user, both on the sender and the receiver. However, these semantics increase memory allocations and copies and in turn affect application memory footprint and performance, especially with large message buffers. In this work we describe how the so-called "zero copy" messaging semantics can be supported in Converse, the message-driven parallel programming framework that is used by Charm++, by implementing support for user-owned buffer transfers in its lower level runtime system, LRTS. These semantics work on user-provided buffers and do not semantically require copies by either the user or the runtime system. We motivate our work by reviewing the existing messaging model in Converse/Charm++, identify its semantic shortcomings, and define new LRTS and Converse APIs to support zero copy communication based on RDMA capabilities. We demonstrate the utility of our new communication interfaces with benchmarks written in Converse. The result is up to 91% of message latency improvement and improved memory usage. These advances will enable future work on user-facing APIs in Charm++.

关键词： Charm plus Converse RDMA parallel programming Asynchronous tasking Communication optimizations

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Anomaly Detection with Graph Attention network for Multimodal IoT Data Monitoring

Anomaly Detection with Graph Attention Network for Multimoda...

引用

International conference on parallel and distributed Systems (ICPADS)

作者： Haoyu Li Yi Gao Wei Dong College of Computer Science Zhejiang University China

The popularity of the wireless network and the embedded system has promoted the widespread application of the Internet of Things (IoT) monitoring system which detects anomalies for discovering emergency events to avoid losses by deploying various sensors. In the face of anomaly detection algorithms for multi-dimensional data in the IoT monitoring system, the currently popular method is to implement a model based on the graph neural network (GNN). Graph attention network (GAT), a mutant of GNN, is widely used because of the attention mechanism in capturing multi-dimensional nonlinear features between nodes. However, the GAT’s weakness in processing complex temporal features leads to the fact that anomalies with distinctive temporal features are hard to detect. In this paper, we propose Time-Series Graph Attention network (TSGAT), an innovative GAT-based model for anomaly detection. Given the simplified attention mechanism of the GAT and its weakness in extracting temporal pattern features, we embed the time-series layer to enhance the ability to process time-series data. In addition, we designed the loss function and anomaly scoring mechanism based on data reconstruction features, multidimensional features, and temporal features. TSGAT achieves the improvement of 7%-8% F1 score on two classical public datasets for IoT monitoring: SWaT and WADI, compared with the state-of-the-art GNN-based anomaly detection model.

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Adaboost-based Graph Convolutional networks with Channel Attention for Intrusion Detection in Internet-of-Things 21

Adaboost-based Graph Convolutional Networks with Channel Att...

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21st IEEE International Symposium on parallel and distributed processing with Applications, 13th IEEE International conference on Big Data and Cloud Computing, 16th IEEE International conference on Social Computing and networking and 13th International conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Long, Jing Luo, Cuiting Chen, Ruxin Liang, Wei Qin, Zheng Hunan Normal University College of Information Science and Engineering Changsha410081 China Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan411201 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China

ISBN: (纸本)9798350329223

With the increasing proliferation of Internet of Things (IoT) and industrial big data technologies, while IoT scenarios offer intelligent services, cyberspace faces various types of threats and attacks. Despite Graph Convolutional networks (GCN) having been demonstrated as a potent tool for learning graph data representations, many current IoT intrusion detection methods tend to overlook the adoption of graph-related techniques, given the non-graph-structured nature of most IoT traffic data. This leads to the model design overlooking the potential correlations among variables, thus limiting the accuracy of intrusion detection. In this study, we introduce an integrated intrusion detection model that fuses graph learning with a dual attention mechanism(A-GCA). Initially, our research adopts a combination of GCN and Convolutional Neural networks (CNN) to deeply capture intricate interactions between data nodes. To further enhance the model's feature-learning capability, we incorporate a channel attention mechanism for emphasizing features with pivotal information. Additionally, a multi-head attention mechanism is adopted to amalgamate a wealth of contextual information, ensuring more vivid feature representations. Regarding the training strategy, we employ a variant of Adaboost which uniquely steers weak classifiers from focusing on high-precision fitting of raw data towards precise binary classification tasks, aiming to augment the model's overall performance in intrusion detection. Evaluations and performance analyses using real-world datasets UNSW-NB15 and CICIDS2017 indicate that the proposed method achieves improvements of 2.5% and 3.7% over the best baseline model in the accuracy and F1-score, respectively, relative to the baseline, surpassing other approaches. © 2023 IEEE.

关键词： Adaptive boosting

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Introducing a Stream processing Framework for Assessing parallel Programming Interfaces

Introducing a Stream Processing Framework for Assessing Para...

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euromicro conference on parallel, distributed and network-based processing

作者： Adriano Marques Garcia Dalvan Griebler Luiz G. L. Fernandes Claudio Schepke School of Technology Pontifical Catholic University of Rio Grande do Sul (PUCRS) Porto Alegre Brazil Laboratory of Advanced Research on Cloud Computing (LARCC) Três de Maio Maio Faculty (SETREM) Três de Maio Brazil Federal University of Pampa (UNIPAMPA) Alegrete Brazil

Stream processing applications are spread across different sectors of industry and people's daily lives. The increasing data we produce, such as audio, video, image, and text are demanding quickly and efficiently computation. It can be done through Stream parallelism, which is still a challenging task and most reserved for experts. We introduce a Stream processing framework for assessing parallel Programming Interfaces (PPIs). Our framework targets multi-core architectures and C++ stream processing applications, providing an API that abstracts the details of the stream operators of these applications. Therefore, users can easily identify all the basic operators and implement parallelism through different PPIs. In this paper, we present the proposed framework, implement three applications using its API, and show how it works, by using it to parallelize and evaluate the applications with the PPIs Intel TBB, FastFlow, and SPar. The performance results were consistent with the literature.

关键词： Industries parallel programming distributed databases Computer architecture Streaming media parallel processing Task analysis

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Application of Big Data Technology in Data Prediction Models based on Deep Learning Algorithms

Application of Big Data Technology in Data Prediction Models...

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distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International conference on

作者： Yong He Lijie Ma Foundational Courses Department Wuhan Donghu University Wuhan Hubei China

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

How to solve the problems faced by traditional statistical models such as large scale, variability and high complexity of data? In this regard, this article proposes a GAN algorithm. The article first defines the data prediction problem, defines the problem goal of this article as the prediction of stock prices, and then applies the adversarial generation network to the stock price prediction problem. Research has found that GAN can generate multiple possible stock price sequences, thereby providing multiple possibilities for future trends. Afterwards, the article establishes a prediction model based on big data, and completes the experimental test of the algorithm of this article through the model. The test results show that the data results of this model in the accuracy test are between 89% and 98%. By making full use of large-scale data samples and distributed computing capabilities, the model can better capture the complex associations and patterns in the data.

关键词： Training Computational modeling distributed databases Predictive models Big Data parallel processing Prediction algorithms

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Research on Prototype Tool Improvement System based on Power Internet Reference Application Scenario Model

Research on Prototype Tool Improvement System Based on Power...

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Software Engineering, Social network Analysis and Intelligent Computing (SSAIC), Asia-Pacific conference on

作者： Jun Mu Guangming Fan Wenqing Yang Jia Feng Lintan Sun Yeteng An Rui Yang State Grid Electric Power Research Institute Co. Ltd Nanjing China State Grid Customer Service Center Tianjin China

ISBN: (数字)9798350393682

ISBN: (纸本)9798350393699

The prototype system of power Internet application business risk management is the basis for the research of power Internet operation and maintenance, risk management and fault early warning. Integrate existing simulation data and various computing resources of power system to provide parallel/distributed high-performance computing. A service based grid application development platform is constructed to meet the needs of large-scale power system computing and operation and maintenance monitoring. A distributed resource storage and retrieval mechanism is designed and implemented, which is coordinated by multiple resource monitoring servers. The results show that the prototype system has the characteristics of processing distributed heterogeneous data, utilizing distributed computing power and storing computing results in a decentralized manner. The system meets the requirements of multi regional and multi-level power Internet application business risk management.

关键词： Information resources Prototypes Internet Power systems Maintenance Risk management Resource management

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Hierarchical Computing network Collaboration Architecture for Industrial Internet of Things

Hierarchical Computing Network Collaboration Architecture fo...

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International conference on parallel and distributed Systems (ICPADS)

作者： Zihui Luo Xiaolong Zheng Bo Wang Qifeng Meng Helei Cui Xiaobing Guo Liang Liu Beijing University of Posts and Telecommunications Beijing China School of Computer Science Northwestern Polytechnical University Xi’an China Lenovo Research Beijing PR China

The Industrial Internet of Things (IIoT) is deemed a promising direction to drive a new industrial revolution. However, due to the isolation of the existing OT network and IT network, the requirements of low latency, low jitter, and high reliability for transmission and processing of industrial time-sensitive tasks data traffic in IIoT scenarios with strong dynamic and complex topology face a series of non-trivial challenges. In this paper, we propose a hierarchical computing network collaboration architecture for IIoT based on edge/fog computing. Our architecture is built upon the Time-Sensitive networking (TSN) to flexibly support different requirements of large-scale industrial production applications by constructing the hierarchical computing network collaboration domain, combined with an improved Cyclic Queuing and Forwarding (CQF) scheduling shaper mechanism. We tackle the critical problems of architecture design by presenting three essential components. Moreover, we build and implement our simulation testbed based on Omnet++, and evaluate our design.

关键词： Job shop scheduling Processor scheduling Collaboration Computer architecture Dynamic scheduling Delays Reliability

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An Anti-forensic Method based on RS Coding and distributed Storage 21st

An Anti-forensic Method Based on RS Coding and Distributed S...

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21st International conference on Algorithms and Architectures for parallel processing (ICA3PP)

作者： Jiang, Xuhang Wang, Yujue Ding, Yong Liang, Hai Wang, Huiyong Li, Zhenyu Guilin Univ Elect Technol Sch Comp Sci & Informat Secur Guangxi Key Lab Cryptog & Informat Secur Guilin Peoples R China Pengcheng Lab Cyberspace Secur Res Ctr Shenzhen Peoples R China Guilin Univ Elect Technol Sch Math & Comp Sci Guilin Peoples R China

ISBN: (纸本)9783030953881;9783030953874

The anti-forensics (AF) technology has become a new field of cybercrime. The problems of existing forensic technologies should be considered from criminals' perspective, so as to make improvement to existing AF technologies. There are two types of AF methods, namely, data hiding and destruction, where most AF tools are primarily based on data hiding. If the data can be intercepted by investigators during the AF process, the remaining data may be destroyed by the criminal, which would make investigators obtain nothing about data information. To address this issue, this paper proposes an AF scheme with multi-device storage based on Reed-Solomon codes by combining data hiding and data destruction. The data is divided into multiple out-of-order data blocks and parity blocks, where these blocks are stored separately in different devices. This method can reduce the storage cost and protect the privacy of data. Even if the data is destroyed, it allows AF investigators to recover the data. Security analysis showed that this AF method can prevent malicious, erroneous or invalid files while acquired and ensure data security in data stolen. Theoretical analysis indicated that this method was difficult for investigators but easy for AFer in files recovery. Experimental results demonstrated that the proposed method is effective and has practical efficiency.

关键词： Anti-forensics Digital forensics Encryption Computer crime distributed storage Erasure coding

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Combining Thread Throttling and Mapping to Optimize the EDP of parallel Applications

Combining Thread Throttling and Mapping to Optimize the EDP ...

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euromicro conference on parallel, distributed and network-based processing

作者： Gustavo P. Berned Thiarles S. Medeiros Matheus Serpa Fábio D. Rossi Marcelo C. Luizelli Philippe O. Navaux Antonio Carlos S. Beck Arthur F. Lorenzon Federal University of Pampa - Campus Alegrete Brazil Institute of Informatics - Federal University of Rio Grande do Sul Brazil Science and Technology Farroupilha Federal Institute of Education Brazil

Thread-throttling and mapping strategies have been used together to make better use of hardware resources and improve the energy-delay product (EDP) of high-performance computing (HPC) systems. However, the design space exploration significantly grows with the increasing number of cores in those systems, making the task of finding the ideal number of active threads and allocating strategy a challenging task. On top of that, parallel applications present various patterns, such as irregularity, unbalanced computations, or high rates of communications. Given these considerations, we propose ETTM, an EDPaware thread-throttling and mapping optimization strategy that automatically finds an ideal combination of number of threads and thread mapping strategy. With the execution of eighteen well-known benchmarks on three multicore architectures, we show that EDP can be significantly improved when running applications with the solution found by EETM¹.

关键词： Multicore processing Instruction sets distributed databases Computer architecture Benchmark testing Hardware Space exploration

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