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检索条件"任意字段=International Conference on Parallel and Distributed Processing Techniques and Applications"

共 11314 条记录，以下是761-770 订阅

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Optimized NLP Model with Caching, parallel processing and Streamlined I/O

Optimized NLP Model with Caching, Parallel Processing and St...

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international conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： B. Rajalakshmi C Nagamanjula Rani G. Vijayasekaran S Mahesh Kumar Shabrish B Hegde Sai Srikanth Computer Science and Engineering New Horizon College Of Engineering Bangalore India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

This research explores optimization strategies employed within ***, an advanced question generation system driven by natural language processing (NLP) and machine learning (ML) algorithms. The study delves into three pivotal areas of optimization: caching, parallel processing, and streamlining I/O operations. By implementing caching mechanisms using Python’s ***_cache, parallelization via *** and multiprocessing modules, and streamlining I/O operations through efficient batching and buffering techniques, significant enhancements in time and space complexity are realized. The effectiveness of these optimization approaches is empirically evaluated, showcasing their profound impact on the efficiency and scalability of the *** platform. This research contributes to the advancement of question generation systems, providing valuable insights into effective optimization strategies for NLP applications, such as question generation, semantic analysis, and language modeling.

关键词： Runtime Computational modeling Scalability Semantics Machine learning parallel processing Question generation

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Proceedings - 2022 IEEE international conference on Cloud Engineering, IC2E 2022

Proceedings - 2022 IEEE International Conference on Cloud En...

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10th IEEE international conference on Cloud Engineering, IC2E 2022

ISBN: (纸本)9781665491150

The proceedings contain 33 papers. The topics discussed include: network emulation in large-scale virtual edge testbeds: a note of caution and the way forward;an end-to-end framework for benchmarking edge-cloud cluster management techniques;decentralized computation market for stream processing applications;efficient transmission and reconstruction of dependent data streams via edge sampling;get your memory right: the crispy resource allocation assistant for large-scale data processing;streaming vs. functions: a cost perspective on cloud event processing;Fusionize: improving serverless application performance through feedback-driven function fusion;pay-as-you-train: efficient ways of serverless training;and magpie: automatically tuning static parameters for distributed file systems using deep reinforcement learning.

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parallel and hierarchically-distributed Shoreline Alert Model (SAM)

Parallel and hierarchically-distributed Shoreline Alert Mode...

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Ciro Giuseppe de Vita Gennaro Mellone Aniello Florio Catherine Alessandra Torres Charles Diana Di Luccio Marco Lapegna Guido Benassai Giorgio Budillon Raffaele Montella Dep. of Science and Technologies University of Naples “Parthenope” Naples Italy Dep. of Computer Science and Engineering University of Madrid Carlos III Madrid Spain Dept. of Mathematics and Applications University of Naples “Federico II” Naples Italy

In this paper, the Shoreline Alert Model (SAM) is presented as a component of a computation platform based on workflows dedicated to extreme weather/marine event simulation. The model aims to mitigate the effects of global change by providing decision-makers, scientists, and engineers with a novel, next-generation tool set for facing extreme weather events and implementing related management or emergency responses. SAM uses a parallelization schema, allowing users to run it on heterogeneous parallel architectures. As a result, SAM produces approximately 24 times faster results than the baseline when using shared memory with distributed memory and dealing with about 20,000 transects along the Campania coastline. The system is based on the algorithms of the open-source numerical models WRF (Weather Research and Forecasting) and WW3 (Wave-watch III) implemented with refraction and shoaling routines together with run-up equations to form the modeling chain used for coastal flooding assessment.

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Using MapReduce and Time Series Analysis to Defend Against DDoS Attacks 1

Using MapReduce and Time Series Analysis to Defend Against D...

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1st international conference on Recent Developments in Electronics and Communication Systems, RDECS 2022

作者： Pramilarani, K. Vasanthi Kumari, P. Vinod Kumar, Y.S. DSU NHCE Dept. of Computer Applications DSU Dept. of CSE NHCE

ISBN: (纸本)9781643683607

Detection of distributed Denial Of Service (DDoS) attacks have been increasing rapidly in the recent years mainly causing a severe risk to security of network systems in large and also occupying resources of computer systems such as computer power, memory, band width and many more. Detection and Prevention of DDoS attacks at fasters not only helps in the network security but also increases computer system performance by not occupying computer resources. Therefore, it has become very essential in today's fast growing world to detect and prevent DDoS attacks. The proposed system makes use of Hadoop Framework which includes the techniques of map reduce that uses the log files as the datasets dividing them into smaller multiple chunks for easy processing and training the proposed model for detecting and preventing the network systems from DDoS attacks at faster manner.. © 2023 The authors and IOS Press.

关键词： Network security

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Deep learning methods for malicious URL detection using embedding techniques as Logistic Regression with Lasso penalty and Random Forest 7

Deep learning methods for malicious URL detection using embe...

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

作者： Thakur, Isha Panda, Kajal Kumar, Sanjeev Centre for Development of Advanced Computing Cyber Security Technology Division Mohali India

ISBN: (纸本)9781665454018

In the Internet ecosystem, URLs (Uniform Resource Locators) are widely used to propagate malicious infections through spam, spear-phishing, drive-by-download exploitation, malware embedding, etc. The blacklisting, pattern analysis, and signature-matching approaches are widely used to detect such threats. However, these techniques are very effective in detecting known types of malicious URLs but unable to detect new types of attacks launched by the malicious URLs. Moreover, traditional machine learning-based malicious URL detection is highly dependent upon manual feature engineering, which is a costly approach. , This research study proposes deep learning-based malicious URL detection with less dependent on manual feature engineering to overcome the above-mentioned challenges. The Natural Language processing (NLP) technique, i.e., Term Frequency-Inverse Document Frequency (TF-IDF) vectorizer, along with the N-gram parameter, is used for extracting the features. Afterward, two feature selection techniques as Embedded methods are used - Logistic Regression with L1 (Lasso) penalty and Random Forest Selection. Neural network models such as Deep Neural Networks (DNN), Long Short-Term Memory (LSTM), and Convolution Neural Networks (CNN) are constructed to detect malicious URLs. The experiment results demonstrate that the DNN model gives the best performance results as 96.95% accuracy, 99% precision, 100% recall, and 99% F1-score with logistic regression along with the L1 (Lasso) penalty as a feature selection method. Finally, this study compares state-of-art implemented deep learning methods for malicious URL detection. © 2022 IEEE.

关键词： Inverse problems

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Universal Filter Array with Memristive Crossbar

Universal Filter Array with Memristive Crossbar

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IEEE conference on Nanotechnology

作者： Akhila Remanan Anitha Gopi Aswani A R Alex James School of Electronic Systems and Automation Digital University Kerala India

ISBN: (数字)9798350386240

ISBN: (纸本)9798350386257

This study explores the integration of memristor crossbar as filter arrays for image processing applications exploit the various filtering techniques. Memristor crossbar arrays offer a promising platform for parallel processing and efficient implementation of filtering operations due to their dense and scalable architecture. Configuring each column in the crossbar array to act as a filter, it becomes possible to perform multiple filtering operations simultaneously on input images. This research investigates the feasibility and performance of utilizing memristor crossbar arrays as filter arrays with different filter structures and random dropouts in image processing. This analysis focusing on the potential of memristor based reconfigurable filter arrays in advancing the field of image processing,

关键词： Filtering Image processing Memristors Focusing parallel processing Nanotechnology

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A highly scalable high-performance Lagrangian transport and diffusion model for marine pollutants assessment

A highly scalable high-performance Lagrangian transport and ...

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Raffaele Montella Diana Di Luccio Ciro Giuseppe de Vita Gennaro Mellone Marco Lapegna Gloria Ortega Livia Marcellino Enrico Zambianchi Giulio Giunta Dept. of Science and Technologies University of Naples “Parthenope” Naples Italy Dept. of Mathematics and Applications University of Naples “Federico II” Naples Italy Dept. of Informatics University of Almeria Almeria Spain

While using High-Performance Computing (HPC) for precise and accurate air quality forecasts is a common issue, similar services devoted to marine pollution in coastal areas remain challenging. This paper presents Water quality Community Model Plus Plus (WaComM++) leveraging a parallelization schema enabling the users to run it on heterogeneous parallel architectures. We evaluated the proposed model under several execution approaches using a real-world application for pollutants forecast in the Gulf of Napoli (Campania, Italy). As a result, WaComM++ has produced results 657K times faster than the sequential run (taking into account the Particles' Outer Cycle and not considering the particle domain distribution) when using distributed and shared memory with multi-GPUs dealing with about 25 million particles.

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Faster As Well As Early Measurements from Big Data Predictive Analytics Model 1

Faster As Well As Early Measurements from Big Data Predictiv...

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1st international conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021

作者： Gupta, Shashi Kant Tiwari, Sharda Jamil, Azlin Abd Singh, Prabhdeep Department of CS&E Integral University UP Lucknow India Department of CS&E Faculty of Engineering and Technology Dr Shakuntala Misra National Rehabilitation University UP Lucknow India Universiti Teknologi Malaysia School of Computer Applications BBD University UP Lucknow India

ISBN: (纸本)9781607685395

Data processing in large scale system and analyses is the key problems of today's distributed Systems in real-time and close to real time. These systems should be able, while meeting these constraints, to process large-scale data inputs, to expand to Terabytes or larger. Volume of processing the data over the systems of large scale becomes too large. These systems use parallelization of the data and similar techniques to increase performance. The standards are increasing, though: data analyses are expected almost in real time. Among all the data treatment models Map Reduce is mostly adopted. Due to the processing of data design in this method, once work is finished, the results are returned. The growth of data is not always a preferred batch operating environment: large numbers of applications can benefit from early results. The online aggregation method used in connection databases was first discussed. In this work analysis we discuss techniques that can allow early results to be estimated. We are proposing some changes to the online system for Map Reduce. We show that the properties required for accurate evaluation of the effects of our proposed device design changes. The reduction of data bias algorithm and sampling at the block levels are provided. Consequently, with a series of particular applications and sets of data the implementation of the proposed design of the system is defined and evaluated. © The Electrochemical Society

关键词： Big Data Data processing Data sampling Hadoop distributed file system MapReduce framework

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distributed Machine Learning based Mitigating Straggler in Big Data Environment

Distributed Machine Learning based Mitigating Straggler in B...

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IEEE international conference on Communications (ICC)

作者： Lu, Haodong Wang, Kun Nanjing Univ Posts & Telecommun Coll Internet Things Nanjing Peoples R China Univ Calif Los Angeles Dept Elect & Comp Engn Los Angeles CA USA

ISBN: (纸本)9781728171227

In big data era, utilizing the parameter server paradigm has been regarded as an efficient and practical way to improve performance in processing deep learning (DL) applications. One of the main problems is that straggler greatly hinders DL training progress, but the previous methods cannot fully consider the resource utilization of the cluster when dealing with straggler. To mitigate straggler problem in parameter server, we propose a Deep Reinforcement Learning (DRL)-based framework called distributed Actor-critic Reinforcement Learning (DARL) that can automatically adapt each worker's training load to the dynamic cluster without parameter settings. DARL employs state-of-the-art techniques to stabilize training and improve convergence, including distributed framework, multiple actors and prioritized experience replay. Meanwhile, we also apply our customized experience sampling method to fully exploit potentially good samples. Experiments using real DL workloads show that DARL outperforms the representative Bulk Synchronous parallel (BSP) scheme by 57.8% and Stale Synchronous parallel (SSP) by 503% in terms of per-iteration time in heterogeneous environment.

关键词： Parameter Server Straggler Deep Reinforcement Learning

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Noise-Resilient Empirical Performance Modeling with Deep Neural Networks 35

Noise-Resilient Empirical Performance Modeling with Deep Neu...

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35th IEEE international parallel and distributed processing Symposium (IPDPS)

作者： Ritter, Marcus Geiss, Alexander Wehrstein, Johannes Calotoiu, Alexandru Reimann, Thorsten Hoefler, Torsten Wolf, Felix Tech Univ Darmstadt Dept Comp Sci Darmstadt Germany Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland

ISBN: (纸本)9781665440660

Empirical performance modeling is a proven instrument to analyze the scaling behavior of HPC applications. Using a set of smaller-scale experiments, it can provide important insights into application behavior at larger scales. Extra-P is an empirical modeling tool that applies linear regression to automatically generate human-readable performance models. Similar to other regression-based modeling techniques, the accuracy of the models created by Extra-I' decreases as the amount of noise in the underlying data increases. This is why the performance variability observed in many contemporary systems can become a serious challenge. In this paper, we introduce a novel adaptive modeling approach that makes Extra-P more noise resilient, exploiting the ability of deep neural networks to discover the effects of numerical parameters, such as the number of processes or the problem size, on performance when dealing with noisy measurements. Using synthetic analysis and data from three different case studies, we demonstrate that our solution improves the model accuracy at high noise levels by up to 25% while increasing their predictive power by about 15%.

关键词： Performance analysis performance modeling deep learning artificial neural networks high performance computing parallel processing

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