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

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Selective Caching: Avoiding Performance Valleys in Massively parallel Architectures 28

Selective Caching: Avoiding Performance Valleys in Massively...

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28th Euromicro international conference on parallel, distributed and Network-Based processing (PDP)

作者： Jadidi, Amin Kandemir, Mahmut T. Das, Chita R. Cadence Design Syst San Jose CA 95134 USA Penn State Univ Dept Comp Sci & Engn University Pk PA 16802 USA

ISBN: (纸本)9781728165820

Emerging general purpose graphics processing units (GPGPU) make use of a memory hierarchy very similar to that of modern multi-core processors they typically have multiple levels of on-chip caches and a DDR-like off-chip main memory. In such massively parallel architectures, caches are expected to reduce the average data access latency by reducing the number of off-chip memory accesses;however, our extensive experimental studies confirm that not all applications utilize the on-chip caches in an efficient manner. Even though GPGPUs are adopted to run a wide range of general purpose applications, the conventional cache management policies are incapable of achieving the optimal performance over different memory characteristics of the applications. This paper first investigates the underlying reasons for inefficiency of common cache management policies in GPGPUs. To address and resolve those issues, we then propose (i) a characterization mechanism to analyze each kernel at runtime and, (ii) a selective caching policy to manage the flow of cache accesses. Evaluation results of the studied platform show that our proposed dynamically reconfigurable cache hierarchy improves the system performance by up to 105% (average of 27%) over a wide range of modern GPGPU applications, which is within 10% of the optimal improvement.

关键词： parallel architectures

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On the Detection of Silent Data Corruptions in HPC applications Using Redundant Multi-threading

On the Detection of Silent Data Corruptions in HPC Applicati...

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26th international conference on parallel and distributed Computing (Euro-Par)

作者： Perez, Diego Ropars, Thomas Meneses, Esteban Costa Rica Inst Technol Sch Comp San Jose Costa Rica Univ Grenoble Alpes CNRS Grenoble INP Grenoble France Costa Rica Natl High Technol Ctr Adv Comp Lab San Jose Costa Rica

ISBN: (纸本)9783030715939;9783030715922

This paper studies the use of Redundant Multi-Threading (RMT) to detect Silent Data Corruptions in HPC applications. To understand if it can be a viable solution in an HPC context, we study two software optimizations to reduce RMT performance overhead by reducing the amount of data exchanged between the replicated threads. We conduct experiments with representative HPC workloads to measure the performance gains obtained through these optimizations, and the error detection coverage they achieve. In the best case, when running on a processor that features Simultaneous Multi-Threading, our results show that the overhead can be as low as 1.4x without significantly reducing the ability to detect data corruptions.

关键词： HPC Silent data corruptions Redundant multi-threading

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2nd international conference on Computing, Communication, and Learning, CoCoLe 2023

2nd International Conference on Computing, Communication, an...

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2nd international conference on Computing, Communication, and Learning, CoCoLe 2023

ISBN: (纸本)9783031569975

The proceedings contain 25 papers. The special focus in this conference is on Computing, Communication, and Learning. The topics include: Yolo and RetinaNet Ensemble Transfer Learning Detector: Application in Pavement Distress;stock Market Prediction Performance Analysis by Using Machine Learning Regressor techniques;nearest Neighbor and Decision Tree Based Cloud Service QoS Classification;object Recognition Through Content Based Feature Extraction and Classification of Sounds in IoT Environment;detection of Malicious Activity on Credit Cards Using Machine Learning;agent-Based Modeling for Optimum Location Selection: Enhancing Day-Ahead Market Analysis for Microgrid Integration;a Comparative Approach for Skin Cancer Detection Using Artificial Bee Colony;a Hybrid Approach to Enhance Software Quality by Sentiment Analysis of Developer;unsupervised Sentiment Analysis of Amazon Fine Food Reviews Using Fuzzy Logic;sentiment Analysis of Crypto Currency Trading applications in India Using Machine Learning;Graph Similarity Join (GSJ) Approach to Detect Near Duplicate Text Documents;three Factor Authentication Scheme for Telecare Medical Information System;an Effective Threshold Based Technique for Retinal Image Blood Vessel Segmentation on Fundus Image Using Average and Gaussian Filters;substantiation System Using Facial Recognition and Principal Component Analysis;impact of Stress on Faculties’ Attitudes: A Study of Higher Education Institutes;dynamic Cross-Layer Communication Design for Multi-objective Optimization in Wireless Sensor Networks;Implementation of Weighted parallel Interference Cancellation Technique for Multi User Detection in UWA Communication;analysis and Comparison of Firefly Algorithm for Measuring Convergence Rate in distributed Environment;A Comparative Study on Social Behavior of Tribes and Non Tribes in ITDA Paderu by Using Network Science;raster Big Data processing Using Spark with GeoTrellis.

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Automatic English Translation using Artificial Recurrent Neural Network with Machine Translation

Automatic English Translation using Artificial Recurrent Neu...

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distributed Systems, Computer Networks and Cybersecurity (ICDSCNC), international conference on

作者： Jianxun Guo Beibei Zheng Lei Zhang Biao Li Haolin Guo Ming Chen Tianjin University of Technology and Education Tianjin China Zhuobei Technology (Wuhan) Co. Ltd Wuhan China Tianjin Data Development Center Tianjin China Tianjin Academy of Fine Arts Tianjin China

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

In recent years, automated recognition technique for English translation error recognition yields poor accuracy recognition results due to its limited ability for semantic analysis. Consequently, this research creates an automatic identification approach for Machine Translation (MT) error detection based on Artificial Intelligence (AI). Thus, this research developed an intelligent identification method known as Artificial Recurrent Neural Network with Machine Translation (ARNN-MT) in order to recognize English translation errors. Context-learning from previous words is used by ARNN sequential data to detect translation issues. MT uses these networks to predict accurate translations. These hybridized ARNN-MT techniques combined with rule-based process, and learned patterns to enhance error detection. Consequently, ARNN-MT improves phrase flow and able to resolve specific translation problems while providing a logical translation in context. When compared to existing algorithms, including Machine English Translation Errors-Multifeatured Fusion (METE-MF), Double-Recurrent Neural Network (Double RNN), and Radio Magnetic Pronunciation Recording Devices (RMPRD), the results demonstrated that the proposed ANN-MT method performed better in terms of accuracy, of 99.96%.

关键词： Performance evaluation Translation Accuracy Recurrent neural networks Computational modeling Semantics parallel processing Prediction algorithms Machine translation Artificial intelligence

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international Workshops on BDMS, BDQM, GDMA, IWBT, MAQTDS, and PMBD 2022, held in conjunction with the 27th international conference on Database Systems for Advanced applications, DASFAA 2022

International Workshops on BDMS, BDQM, GDMA, IWBT, MAQTDS, a...

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international Workshops on BDMS, BDQM, GDMA, IWBT, MAQTDS, and PMBD 2022, held in conjunction with the 27th international conference on Database Systems for Advanced applications, DASFAA 2022

ISBN: (纸本)9783031112164

The proceedings contain 30 papers. The special focus in this conference is on Database Systems for Advanced applications. The topics include: Chinese Spelling Error Detection and Correction Based on Knowledge Graph;construction and Application of Event Logic Graph: A Survey;enhancing Low-Resource Languages Question Answering with Syntactic Graph;profile Consistency Discrimination;h-V: An Improved Coding Layout Based on Erasure Coded Storage System;astral: An Autoencoder-Based Model for Pedestrian Trajectory Prediction of Variable-Length;a Survey on Spatiotemporal Data processing techniques in Smart Urban Rail;fast Vehicle Track Counting in Traffic Video;TSummary: A Traffic Summarization System Using Semantic Words;a Novel Method to Create Synthetic Samples with Autoencoder Multi-layer Extreme Learning Machine;attention-Cooperated Reinforcement Learning for Multi-agent Path Planning;big Data-Driven Stable Task Allocation in Ride-Hailing Services;weighted Mean-Field Multi-Agent Reinforcement Learning via Reward Attribution Decomposition;Evaluating Presto and SparkSQL with TPC-DS;optimizing the Age of Sensed Information in Cyber-Physical Systems;aggregate Query Result Correctness Using Pattern Tables;time Series Data Quality Enhancing Based on Pattern Alignment;research on Feature Extraction Method of Data Quality Intelligent Detection;big Data Resources to Support Research Opportunities on Air Pollution Analysis in India;air Quality Data Collection in Hyderabad Using Low-Cost Sensors: Initial Experiences;pattern Mining: Current Challenges and Opportunities;visualizing Spatio-temporal Variation of Ambient Air Pollution in Four Small Towns in India;why Not to Trust Big Data: Discussing Statistical Paradoxes;localized Metric Learning for Large Multi-class Extremely Imbalanced Face Database;top-k Dominating Queries on Incremental Datasets;Collaborative Blockchain Based distributed Denial of Service Attack Mitigation Approach with IP Reputation System;model-Driven Developm

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Transformation from DMS to ADMS in Power Distribution Utilities: Challenges and Lessons Learned

Transformation from DMS to ADMS in Power Distribution Utilit...

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international conference on High Voltage Engineering and Application, ICHVE

作者： Muhra Alseiari Ahmed Tawfeek Omar Al Ahmad Mohamad Habra SCADA Support Department Al Ain Distribution Company Alain UAE Operation Planning and Studies Department Al Ain Distribution Company Alain UAE Network Operation Department Al Ain Distribution Company Alain UAE

ISBN: (数字)9798350374988

ISBN: (纸本)9798350374995

This paper illustrates the challenges faced and lessons learned during the transformation process in Al Ain Distribution Company (AADC) from Distribution Management System (DMS) to Advanced Distribution Management System (ADMS) in High Voltage (33kV, 11kV) Power Network Control Center. The old DMS system was commissioned in 2005 and got replaced by advanced distribution management system in 2023/2024. Starting from issues that emerged during system design, configuration, and installation, and extending through system commissioning, testing and parallel operation with the old DMS to the final stage of the ADMS being fully online; the lessons learnt are discussed to provide best practices that can be applied by other utilities to overcome such challenges. The transformation from DMS to ADMS can be generally segregated to three different milestones. Firstly, system upgrade and enhancement requirements like system sizing, modeling of new signals and state of the art equipment. Secondly, getting Advanced features and functionalities requirements of integration with other IT enterprise systems and cyber security requirements. Finally, the project execution requirements of data migration, parallel operation, commissioning and testing. For successful transformation process, the teams shall be made ready to work under pandemic situations (like COVID-19) in hybrid mode instead of complete remote work that negatively impacted the project progress and quality. Integration and modeling of new power equipment like capacitor banks, distributed Generators (DGs), and storage systems shall be considered to achieve best results of online power applications.

关键词： Pandemics Process control Power distribution High-voltage techniques Companies Remote working Generators

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Practical parallelization of Scientific applications 28

Practical Parallelization of Scientific Applications

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28th Euromicro international conference on parallel, distributed and Network-Based processing (PDP)

作者： Cesare, Valentina Colonnelli, Iacopo Aldinucci, Marco Univ Turin Phys Dept Turin Italy Univ Turin Comp Sci Dept Turin Italy

ISBN: (纸本)9781728165820

This work aims at distilling a systematic methodology to modernize existing sequential scientific codes with a limited re-designing effort, turning an old codebase into modern code, i.e., parallel and robust code. We propose an automatable methodology to parallelize scientific applications designed with a purely sequential programming mindset, thus possibly using global variables, aliasing, random number generators, and stateful functions. We demonstrate the methodology by way of an astrophysical application, where we model at the same time the kinematic profiles of 30 disk galaxies with a Monte Carlo Markov Chain (MCMC), which is sequential by definition. The parallel code exhibits a 12 times speedup on a 48-core platform.

关键词： loop parallelism checkpointing scientific code open MP

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Designing neuromorphic architectures: towards an ultra low power AI

Designing neuromorphic architectures: towards an ultra low p...

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international conference on Embedded & distributed Systems (EDiS)

作者： Pierre Boulet Laboratory CRIStAL University of Lille France

ISBN: (数字)9798350397543

ISBN: (纸本)9798350397550

In the “bio-inspired information processing” project of the IRCICA interdisciplinary institute, we tackle the scientific challenges of the emerging neuromorphic architectures. These computer architectures mimic the brain by handling the information as spike trains and by processing this information with spiking neural networks. They have a strong potential for ultra low power artificial intelligence. Based on our last 10 years of research, we will present the state-of-the-art of these architectures, the applications we focus on, and the scientific hot topics.

关键词： Computer architecture Low-power electronics Computer science Professional societies parallel processing Neuromorphic engineering Nanoelectronics

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Rethinking memory and communication costs for efficient data parallel training of large language models 24

Rethinking memory and communication costs for efficient data...

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Proceedings of the 38th international conference on Neural Information processing Systems

作者： Hanxiao Zhang Lin Ju Chan Wu Jinjing Huang Youshao Xiao Zhenglei Zhou Zhiming Fan Zhaoxin Huan Siyuan Li Fanzhuang Meng Lei Liang Xiaolu Zhang Jun Zhou Ant Group Hangzhou China

ISBN: (纸本)9798331314385

Recently, various strategies for distributed training of large language models (LLMs) have been proposed. By categorizing them into basic strategies and composite strategies, we have discovered that existing basic strategies provide limited options in specific scenarios, leaving considerable room for optimization in training speed. In this paper, we rethink the impact of memory and communication costs on the training speed of LLMs when employ data parallelism based techniques. We take the impact of intra- and inter-group communication performance disparities into account, and then propose a new set of basic strategies named the Partial Redundancy Optimizer (PaRO). PaRO Data parallelism (PaRO-DP) accelerates LLM training through refined model state partitioning and tailored training procedures. Additionally, PaRO Collective Communications (PaRO-CC) speeds up collective communication operations by rearranging the topology. We also propose a guideline for choosing different DP strategies based on simple quantitative calculations, which yields minimal ranking errors. Our experiments show that PaRO improves the training speed of LLMs by up to 266% that of ZeRO-3 as basic DP strategies. Moreover, employing PaRO-CC independently for model parallel strategies, such as Megatron, can also boost the training speed by 17%.

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Dynamic Task Shaping for High Throughput Data Analysis applications in High Energy Physics

Dynamic Task Shaping for High Throughput Data Analysis Appli...

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international Symposium on parallel and distributed processing (IPDPS)

作者： Ben Tovar Ben Lyons Kelci Mohrman Barry Sly-Delgado Kevin Lannon Douglas Thain Department of Computer Science and Engineering University of Notre Dame Department of Physics University of Notre Dame

distributed data analysis frameworks are widely used for processing large datasets generated by instruments in scientific fields such as astronomy, genomics, and particle physics. Such frameworks partition petabyte-size datasets into chunks and execute many parallel tasks to search for common patterns, locate unusual signals, or compute aggregate properties. When well-configured, such frameworks make it easy to churn through large quantities of data on large clusters. However, configuring frameworks presents a challenge for end users, who must select a variety of parameters such as the blocking of the input data, the number of tasks, the resources allocated to each task, and the size of nodes on which they run. If poorly configured, the result may perform many orders of magnitude worse than optimal, or the application may even fail to make progress at all. Even if a good configuration is found through painstaking observations, the performance may change drastically when the input data or analysis kernel changes. This paper considers the problem of automatically configuring a data analysis application for high energy physics (TopEFT) built upon standard frameworks for physics analysis (Coffea) and distributed tasking (Work Queue). We observe the inherent variability within the application, demonstrate the problems of poor configuration, and then develop several techniques for automatically sizing tasks to meet goals of resource consumption, and overall application completion.

关键词： distributed processing High energy physics Data analysis Instruments Genomics Throughput Task analysis

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