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Machine Learning techniques for Understanding and Predicting Memory Interference in CPU-GPU Embedded Systems 29

Machine Learning Techniques for Understanding and Predicting...

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29th IEEE international conference on Embedded and Real-Time Computing Systems and applications, RTCSA 2023

作者： Masola, Alessio Capodieci, Nicola Rouxel, Benjamin Franchini, Giorgia Cavicchioli, Roberto University of Modena and Reggio Emilia Department of Physics Informatics and Mathematics Italy

ISBN: (纸本)9798350337860

Nowadays, heterogeneous embedded platforms are extensively used in various low-latency applications, including the automotive industry, real-time IoT systems, and automated factories. These platforms utilize specific components, such as CPUs, GPUs, and neural network accelerators for efficient task processing and to solve specific problems with a lower power consumption compared to more traditional systems. However, since these accelerators share resources such as the global memory, it is crucial to understand how workloads behave under high computational loads to determine how parallel computational engines on modern platforms can interfere and adversely affect the system's predictability and performance. One area that remains unclear is the interference effect on shared memory resources between the CPU and GPU: more specifically, the latency degradation experienced by GPU kernels when memory-intensive CPU applications run concurrently. In this work, we first analyze the metrics that characterize the behavior of different kernels under various board conditions caused by CPU memory-intensive workloads on a Nvidia Jetson Xavier. Then, we exploit various machine learning methodologies aiming to estimate the latency degradation of kernels based on their metrics. As a result of this, we are able to identify the metrics that could potentially have the most significant impact when predicting the kernels completion latency degradation. © 2023 IEEE.

关键词： Graphics processing unit

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Automated Power Point Generation using Natural Language processing techniques 9

Automated Power Point Generation using Natural Language Proc...

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9th international conference on Smart Computing and Communications, ICSCC 2023

作者： Thomas, Sebin John, V.G. Chacko, Josin Shajahan, Mariyam Sunny, Sharon Amal Jyothi College of Engineering Dept. of Computer Science & Eng. Kottayam India

ISBN: (纸本)9798350314090

Presentations have become an important part of the work life whether it is a student or an employee or a businessman. But creating a presentation is a time consuming process. The proposed method aims to reduce the amount of work put into creating a presentation by automatically generating an outline or a framework of the presentation based on text input. The system is a website that will be able to convert a text file or a pdf document into a presentation. If it is a text document, segments of text are created by analyzing the text and grouping sentences that belong to the same topic. Then, each of these segments will be summarized and key points will be generated. These points will be distributed over several slides. Appropriate titles will be generated for each slide based on the contents. If it is a pdf document, the titles are extracted from the document itself. The segments are already present as pdf documents and have a structure. The presentation can be exported as *** file which lets the user to edit the file easily and make any changes if necessary. The system uses Google App Script to generate a file ***, and uses API's to summarize text, generate key points and to generate titles. © 2023 IEEE.

关键词： Natural language processing systems

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An Analysis of Representative Works of Japanese Literature Based on Emotions and Topics 1

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37th international conference on Advanced Information Networking and applications, AINA 2023

作者： Amano, Miki Tsumuraya, Kenshin Uehara, Minoru Adachi, Yoshihiro Department of Information Science and Arts Toyo University Saitama3508585 Japan Graduate School of Information Sciences and Arts Toyo University Saitama3508585 Japan RIIT Toyo University Saitama3508585 Japan

ISBN: (数字)9783031284519

ISBN: (纸本)9783031284502

In literary research, subjective analysis by researchers and critics using artisanal analog methods is still the mainstream approach. In contrast, text-processing techniques that make full use of machine learning are already being actively applied to the analysis of Internet public opinion and various types of reviews. In this study, we analyzed the works of representative writers of modern Japanese literature in terms of emotions and topics. For emotion-based analysis, we combined a dictionary-based emotional analysis technique with the RoBERTa machine learning model. For topic-based analysis, we used a method for clustering distributed representations generated by Sentence-BERT. The method proposed in this study for analyzing the characteristics of literary works using machine learning techniques represents one of the most advanced and objective digital approaches to literary research. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Text processing

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Towards a Data Collection Quality Model for Big Data applications 24th

Towards a Data Collection Quality Model for Big Data Applica...

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24th international conference on Business Information Systems (BIS)

作者： Abdallah, Mohammad Hammad, Alaa AlZyadat, Wael Zaytoonah Univ Jordan Fac Sci & Informat Technol Amman Jordan

ISBN: (纸本)9783031042164;9783031042157

Big Data and its uses are widely used in many applications and fields;artificial information, medical care, business, and much more. Big Data sources are widely distributed and diverse. Therefore, it is essential to guarantee that the data collected and processed is of the highest quality, to deal with this large volume of data from different sources with caution and attention. Consequently, the quality of Big Data must be fulfilled starting from the beginning;data collection. This paper provides a viewpoint on the key Big Data collection Quality Factors that need to be considered every time the data are captured, generated, or created. This study proposes a quality model that can help create and measure data collection methods and techniques. However, the quality model is still introductory and needs to be further investigated.

关键词： Big Data Data collection Data quality Data collection quality Data collection quality model

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AFMA-Track: Adaptive Fusion of Motion and Appearance for Robust Multi-object Tracking 27th

AFMA-Track: Adaptive Fusion of Motion and Appearance for ...

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27th international conference on Pattern Recognition, ICPR 2024

作者： Liao, Wei Luo, Lei Zhang, Chunyuan College of Computer Science and Technology National University of Defence Technology Changsha China Science and Technology on Parallel and Distributed Processing Laboratory College of Computer Science and Technology National University of Defense Technology Changsha China

ISBN: (纸本)9783031784439

Motion and appearance cues play a crucial role in Multi-object Tracking (MOT) algorithms for associating objects across consecutive frames. While most MOT methods prioritize accurate motion modeling and distinctive appearance representations, the use of appearance and motion cues is often confined to simplistic association techniques. For instance, fixed weights are commonly employed to combine the intersection-over-union (IoU) matrix and appearance similarity matrix, yielding an association cost matrix. To harness the full potential of motion and appearance cues across diverse scenarios, we propose an innovative approach that dynamically balances motion and appearance cues based on scene and object information during the association process. Furthermore, we introduce a new mechanism for updating appearance representations, effectively mitigating noise introduced by occlusion. Our method demonstrates state-of-the-art performance on the MOT17 and MOT20 test sets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Object detection

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Efficient Multi-GPU Graph processing with Remote Work Stealing 39

Efficient Multi-GPU Graph Processing with Remote Work Steali...

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39th IEEE international conference on Data Engineering, ICDE 2023

作者： Meng, Ke Geng, Liang Li, Xue Tao, Qian Yu, Wenyuan Zhou, Jingren Alibaba Group China

ISBN: (纸本)9798350322279

Graph algorithms support a broad spectrum of big data applications. A typical approach to scale graph algorithms is to run in a distributed and parallel setting with multiple processing devices. The approach requires balanced and effective utilization of computation, memory, and communication resources across devices. To address the problem, a large number of studies have been conducted, such as graph partitioning and asynchronous computation. However, there are still many outstanding issues yet to be solved. For example, the workloads can be skewed differently across devices, and between iterations, even with the state-of-the-art graph partitioners. As the graph partitions are typically static, they fall short in capturing the dynamic characteristics with different algorithms, inputs, and progress, leading to poor utilization of resources. Recently, GPUs have been increasingly used to accelerate various graph algorithms. Their highly efficient interconnection technologies, such as NVLink, open new opportunities for us to achieve better resource utilization. In this paper, we analyze the dynamic load-imbalance (DLB) problem and the long tail (LT) problem in multi-GPUs and solve them by adaptive remote work stealing on-the-fly. We first introduce a frontier stealing algorithm to solve the DLB problem, then an ownership stealing algorithm to solve the LT problem. Based on these two algorithms, we developed Gum - a multi-GPU graph processing system with high device utilization. We evaluated Gum on four typical graph algorithms (BFS, WCC, PR, SSSP). The results show that Gum can run up to an order of magnitude faster than Gunrock and Groute, with fewer stragglers and less synchronization overhead. © 2023 IEEE.

关键词： Graphics processing unit

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DeRAG: Decentralized Multi-Source RAG System with Optimized Pyth Network

DeRAG: Decentralized Multi-Source RAG System with Optimized ...

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international Symposium on parallel and distributed processing with applications, ISPA

作者： Junwei Yu Hiroyuki Sato The University of Tokyo Tokyo Japan National Institute of Informatics / The University of Tokyo Tokyo Japan

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Large language models (LLMs) have demonstrated remarkable capabilities in various natural language processing tasks. However, they face significant challenges in accessing up-to-date information and providing verifiable responses, particularly in domain-specific contexts. Retrieval-Augmented Generation (RAG) systems have emerged as a promising solution, but they introduce new vulnerabilities related to data integrity, privacy, and centralization. This paper introduces DeRAG (Decentralized Multi-Source Retrieval-Augmented Generation), a novel approach that leverages blockchain technology and advanced cryptographic techniques to address these challenges. DeRAG incorporates a decentralized multi-source data ecosystem, a RAG-Optimized Pyth Consensus protocol for data validation, and a domain-specific validator network structured as a Directed Acyclic Graph. The system also features an innovative RAG processing Layer with a decentralized index structure based on distributed Hash Tables and content-addressable storage. We present a comprehensive performance evaluation framework that assesses the system’s scalability, efficiency, output quality, and economic model effectiveness in a decentralized context. Results demonstrate that DeRAG significantly enhances the security, efficiency, and privacy of RAG systems, paving the way for more robust and reliable RAG applications in information retrieval and generation.

关键词： Performance evaluation Privacy Scalability Retrieval augmented generation Ecosystems Natural language processing Consensus protocol Indexes Reliability Optimization

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Bucket MapReduce: Relieving the Disk I/O Intensity of Data-Intensive applications in MapReduce Frameworks 29

Bucket MapReduce: Relieving the Disk I/O Intensity of Data-I...

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29th Euromicro international conference on parallel, distributed and Network-Based processing (PDP)

作者： Chen, Kai-Hsun Chen, Hsin-Yuan Wang, Chien-Min Acad Sinica Inst Informat Sci Taipei Taiwan

ISBN: (纸本)9781665414555

Iladoop MapReduce is a software framework for processing vast amounts of data in parallel on large clusters. As data size increases, a need arises to resolve the significant increase in the disk I/O of reducer nodes, which may cause runtime bottlenecks. In response, we propose Bucket MapReduce, a system that utilizes bucket sort and pipeline parallelism to improve the performance of Hadoop MapReduce. Several parameters may influence the performance in Bucket MapReduce, and thus we will further discuss parameter tuning. In this paper, we perform experiments on TeraSort benchmark. Bucket MapReduce successfully reduces local disk I/O by 61% and improves the runtime by 1.39x in 800GB TeraSort benchmark.

关键词： MapReduce Hadoop Disk I/O intensity Bucket sort Pipeline parallelism

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AutoFlow: Hotspot-Aware, Dynamic Load Balancing for distributed Stream processing 21st

AutoFlow: Hotspot-Aware, Dynamic Load Balancing for Distribu...

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

作者： Lu, Pengqi Yue, Yue Yuan, Liang Zhang, Yunquan Chinese Acad Sci Inst Comp Technol State Key Lab Comp Architecture Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9783030953911;9783030953904

Stream applications are widely deployed on the cloud. While modern distributed streaming systems like Flink and Spark Streaming can schedule and execute them efficiently, streaming dataflows are often dynamically changing, which may cause computation imbalance and back-pressure. We introduce AutoFlow, an automatic, hotspot-aware dynamic load balance system for streaming dataflows. It incorporates a centralized scheduler that monitors the load balance in the entire dataflow dynamically and implements state migrations correspondingly. The scheduler achieves these two tasks using a simple asynchronous distributed control message mechanism and a hotspot-diminishing algorithm. The timing mechanism supports implicit barriers and a highly efficient state-migration without global barriers or pauses to operators. It also supports a time-window based load-balance measurement and feeds them to the hotspot-diminishing algorithm without user interference. We implemented AutoFlow on top of Ray, an actor-based distributed execution framework. Our evaluation based on various streaming benchmark datasets shows that AutoFlow achieves good load-balance and incurs a low latency overhead in a highly data-skew workload.

关键词： Stream processing Big data Data skewness Control message Load balance

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Building representative and balanced datasets of OpenMP parallel regions 29

Building representative and balanced datasets of OpenMP para...

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29th Euromicro international conference on parallel, distributed and Network-Based processing (PDP)

作者： Alcaraz, Jordi Sleder, Steven TehraniJamsaz, Ali Sikora, Anna Jannesari, Ali Sorribes, Joan Cesar, Eduardo Univ Autonoma Barcelona Cerdanyola Del Valles Spain Iowa State Univ Ames IA USA

ISBN: (纸本)9781665414555

Incorporating machine learning into automatic performance analysis and tuning tools is a promising path to tackle the increasing heterogeneity of current HPC applications. However, this introduces the need for generating balanced and representative datasets of parallel applications' executions. This work proposes a methodology for building datasets OpenMP parallel code regions patterns. It allows for determining whether a given code region covers a unique part of the pattern input space not covered by the patterns already included in the dataset. The proposed methodology uses hardware performance counter;to represent the execution of the region. which is referred to as the region signature for a given number of cores. Then, a complete representation of the region is built by joining the signatures for every different thread configuration in the system. Next, correlation analysis is performed between this representation and the representation of all the patterns already in the training set. Finally, H . this correlation is helm a given threshold. the region is considered to cover a unique part of the pattern input space and is subsequently added to the dataset. For validating this methodology, an example dataset, obtained from well known benchmarks, has been used to train a carefully designed neural network model to demonstrate that it is able to classify . different patterns of OpenMP parallel regions.

关键词： hardware performance counters machine learning artificial neural networks parallel applications OpeaMP

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