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Workshops held at the 26th international conference on parallel and distributed Computing, Euro-Par 2020

Workshops held at the 26th International Conference on Paral...

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Workshops held at the 26th international conference on parallel and distributed Computing, Euro-Par 2020

ISBN: (纸本)9783030715922

The proceedings contain 27 papers. The special focus in this conference is on parallel and distributed Computing. The topics include: parallelization of the k-means Algorithm in a Spectral Clustering Chain on CPU-GPU Platforms;Management of Heterogeneous Cloud Resources with Use of the PPO;An Open-Source Virtualization Layer for CUDA applications;high Performance Portable Solver for Tridiagonal Toeplitz Systems of Linear Equations;highPerMeshes – A Domain-Specific Language for Numerical Algorithms on Unstructured Grids;Implementation and Evaluation of CUDA-Unified Memory in Numba;Performance Evaluation of Java/PCJ Implementation of parallel Algorithms on the Cloud;parallelizing Automatic Temporal Cognitive Tool for Large-Scale Online Learning Analytics;Experiments Using a Software-distributed Shared Memory, MPI and 0MQ over Heterogeneous Computing Resources;ants-Review: A Privacy-Oriented Protocol for Incentivized Open Peer Reviews on Ethereum;on the Provenance Extraction techniques from Large Scale Log Files: A Case Study for the Numerical Weather Prediction Models;Improving Existing WMS for Reduced Makespan of Workflows with Lambda;predicting Hard Disk Failures in Data Centers Using Temporal Convolutional Neural Networks;On the Detection of Silent Data Corruptions in HPC applications Using Redundant Multi-threading;analysis of Genome Architecture Mapping Data with a Machine Learning and Polymer-Physics-Based Tool;A New parallel Methodology for the Network Analysis of COVID-19 Data;next Generation Blockchain-Based Financial Services;a Digital Voting System for the 21st Century;trustless, Censorship-Resilient and Scalable Votings in the Permission-Based Blockchain Model;p2T: Pay to Transport;Balanced and Compressed Coordinate Layout for the Sparse Matrix-Vector Product on GPUs.

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Large Matrix Multiplication Algorithms: Analysis and Comparison 23

Large Matrix Multiplication Algorithms: Analysis and Compari...

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Proceedings of the 7th international conference on Algorithms, Computing and Systems

作者： Khalil Mouhah Hind Faiz Safae Bourhnane School of Science and Engineering Al Akhawayn University Morocco

ISBN: (纸本)9798400709098

In the realm of computer science and optimization, matrices are fundamental components with a significant role across diverse applications. Matrix multiplication stands as a pivotal operation, and enhancing the efficiency of serial matrix multiplication algorithms holds key importance. This project focuses on optimizing matrix multiplication on a single computing device by exploring algorithmic approaches and optimization techniques. Matrices provide structured data representation, serving purposes ranging from dataset representation, data transformation in computer graphics, image processing, physical system modeling, to mathematical equation solving. This project concentrates specifically on algorithms for matrix multiplication. The standard algorithm computes matrix entries by directly multiplying corresponding input entries, though its efficiency degrades for larger matrices due to its high time complexity. Other algorithms like Strassen's algorithm reduce multiplications or exploit parallelism for efficiency. They employ techniques like divide-and-conquer, fast matrix multiplication, and parallel processing. In this work, these algorithms are analyzed in terms of their concepts, time complexities, and advantages. Practical performance factors like matrix size, hardware architecture, memory needs, cache efficiency, and arithmetic complexity are also explored. Through benchmarking and evaluation on standard platforms, insights into their strengths and weaknesses regarding speed and resource utilization are sought. The ultimate goal is to elevate the efficiency of serial matrix multiplication algorithms in computer science and optimization. By studying and implementing a spectrum of algorithms, the project contributes to optimizing computations and enhancing matrix operation performance across scientific and engineering domains.

关键词： Intel processors Multiprocessor systems parallel computing parallel matrix multiplication Performance optimization Strassen's algorithm

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Accelerating Graph applications Using Phased Transactional Memory 27th

Accelerating Graph Applications Using Phased Transactional M...

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

作者： Morales, Catalina Munoz Murari, Rafael de Carvalho, Joao P. L. Honorio, Bruno Chinelato Baldassin, Alexandro Araujo, Guido Univ Estadual Campinas Inst Comp Campinas Brazil Univ Estadual Paulista UNESP Rio Claro Brazil

ISBN: (纸本)9783030856656;9783030856649

Due to their fine-grained operations and low conflict rates, graph processing algorithms expose a large amount of parallelism that has been extensively exploited by various parallelization frameworks. Transactional Memory (TM) is a programming model that uses an optimistic concurrency control mechanism to improve the performance of irregular applications, making it a perfect candidate to extract parallelism from graph-based programs. Although fast Hardware TM (HTM) instructions are now available in the ISA extensions of some major processor architectures (e.g., Intel and ARM), balancing the usage of Software TM (STM) and HTM to compensate for capacity and conflict aborts is still a challenging task. This paper presents a Phased TM implementation for graph applications, called Graph-Oriented Transactional Memory (GoTM). It uses a three-state (HTM, STM, GLOCK) concurrency control automaton that leverages both HTM and STM implementations to speed-up graph applications. Experimental results using seven well-known graph programs and real-life workloads show that GoTM can outperform other Phased TM systems and lock-based concurrency mechanisms such as the one present in Galois, a state-of-the-art framework for graph computations.

关键词： Hardware transactional memory Software transactional memory Graph processing Large-scale graphs

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Simulating Cyber Attacks and Designing Malware using Python

Simulating Cyber Attacks and Designing Malware using Python

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international conference on Signal processing and Integrated Networks (SPIN)

作者： Akash Ankit Shubham Inder Anuj Sharma Rahul Johari Deo Prakash Vidyarthi SWINGER: Security Wireless IoT Network Group of Engineering and Research University School of Information Communication and Technology Guru Gobind Singh Indraprastha University Dwarka New Delhi India Royal Melbourne Institute of Technology (RMIT University) Melboume Victoria Australia Parallel and Distributed System Lab School of Computer and Systems Sciences Jawaharlal Nehru University New Delhi India

In this Internet era, we have become heavily dependent on the World Wide Web and it has become a basic amenity for humans. A normal user can be trapped easily in this vicious web if not careful enough. It is a field that requires users to be on their toes every time they try to connect to this enormous interconnection of networks. Reports and Surveys have said that in the coming years, Cybercrime could become the biggest threat. In this proposed work, the authors have deployed applications and designed malware for the purpose of simulating various kinds of cyber-attacks so that they could study their effects & affects and ultimately devise solutions to resolve the vulnerabilities in a system. These applications included networking concepts & protocols and a software based keylogger which was developed with no malicious intent, but to analyse the architectural view or design of a malware and to study the stages of a malware attack like entry, distribution, exploitation, infection and execution. Efforts have been made to demonstrate spooting attacks with an idea of identifying vulnerabilities and security loopholes in a particular system that can be exploited by an attacker if not resolved within time.

关键词： Surveys Protocols Malware Internet Web sites Security Signal resolution

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Hybrid Feature Extraction and Deep Learning for Improved Melanoma Diagnosis from Skin Lesion Images

Hybrid Feature Extraction and Deep Learning for Improved Mel...

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

作者： M. Packiamary A. Muthukumaravel Department of Computer Applications Bharath Institute of Higher Education and Research Chennai Tamil Nadu India Department of Arts and Science Bharath Institute of Higher Education and Research Chennai Tamil Nadu India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

The study emphasizes the need for improved melanoma diagnosis by overcoming the limitations of existing techniques. Conventional approaches, which focus on manual feature engineering or standalone deep learning, must precisely capture subtle melanoma patterns. The proposed hybrid System combines traditional image processing with advanced feature extraction and Convolutional Neural Network (CNN) to solve that. The methodology begins with extensive data preprocessing to ensure standardized input quality. Hybrid feature extraction combines edge recognition, texture analysis, and color histogram computing to capture a wide range of low and high-level features important for melanoma diagnosis. The CNN architecture is intended explicitly for melanoma classification, utilizing the power of convolutional layers for feature learning and fully linked layers for classification. The results show that the proposed System outperforms existing systems, with 99.1% accuracy, 98.3% precision, 96% recall, and 96.5% F1 Score in computational metrics. The System's performance is highlighted in comparison analysis, underlining its potential as an advanced diagnostic tool in dermatology, ultimately contributing to improved clinical results and patient care.

关键词： Deep learning Image resolution System performance Scalability Training data Melanoma Feature extraction

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Enhancing Lifetime in Wireless Sensor Networks through Image processing-Based Clustering with Genetic Algorithm Routing

Enhancing Lifetime in Wireless Sensor Networks through Image...

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Soft Computing for Security applications (ICSCSA), international conference on

作者： M. Ilampari M. Amina Begum N Mahesh Babu S. Gopi M. Mythreyee Shanmugavel Deivasigamani Saveetha School of Law SIMATS Department of ECE Oxford College of Engineering Chennai India Department of Information Technology Anurag Engineering College Suryapet Kodad Telangana India Department of CSE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Department of EEE Dr. MGR Educational and Research Institute Chennai Department of Artificial Intelligence and Data Science Jeppiaar Institute of Technology Chennai India

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

Wireless Sensor Networks (WSNs) are a very important technology widely used in diverse application domains of environmental monitoring, disaster management, health care, industrial automation etc. These are networks in which spatially distributed sensor nodes monitor physical or environmental conditions and communications data are processed through a base station. Here, we introduced a novel method for enhancing the efficiency and lifetime of WSNs by using the WSN to process images data and genetic programming (GA) to find the best route. A system model was developed to balance a network consisting of homogenous sensor nodes placed randomly in a particular geographical area, representing realistic scenarios. Each sensor node has limited energy resources, hence the operation of the network was partitioned into clustering phase and routing phase to optimal utilization of energy. To improve both the accuracy and efficiency of clustering, the genetic algorithm then identified the optimal path through these clusters using the best combination of pivot nodes for transmission. The proposed method applies image processing techniques to process sensor data, preprocesses sensor data, and then improves the classification process, clustering results, and the genetic algorithm obtains the more energy-efficient paths. Our extensive evaluation results prove substantial optimizations in network performance, energy efficiency and lifetime over known protocols. This method is expected to be effective in such applications as environmental monitoring, disaster management, and other applications that will utilize reliable and sustainable WSNs.

关键词： Wireless sensor networks Protocols Image processing Disaster management Routing Energy efficiency Environmental monitoring Security Reliability Genetic algorithms

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Autism Disease Detection Using Transfer Learning techniques: Performance Comparison Between Central processing Unit vs Graphics processing Unit Functions for Neural Networks

Autism Disease Detection Using Transfer Learning Techniques:...

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IEEE Annual international Computer Software and applications conference (COMPSAC)

作者： Shapna Akter Hossain Shahriar Alfredo Cuzzocrea Dept. of Computer Science Kennesaw State University Kennesaw USA Dept. of Information Technology Kennesaw State University Kennesaw USA iDEA Lab University of Calabria Rende Italy

Neural network approaches are machine learning methods that are widely used in various domains, such as healthcare and cybersecurity. Neural networks are especially renowned for their ability to deal with image datasets. During the training process with images, various fundamental mathematical operations are performed in the neural network. These operations include several algebraic and mathematical functions, such as derivatives, convolutions, and matrix inversions and transpositions. Such operations demand higher processing power than what is typically required for regular computer usage. Since CPUs are built with serial processing, they are not appropriate for handling large image datasets. On the other hand, GPUs have parallel processing capabilities and can provide higher speed. This paper utilizes advanced neural network techniques, such as VGG16, Resnet50, Densenet, Inceptionv3, Xception, Mobilenet, XGBOOST-VGG16, and our proposed models, to compare CPU and GPU resources. We implemented a system for classifying Autism disease using face images of autistic and non-autistic children to compare performance during testing. We used evaluation matrices such as Accuracy, F1 score, Precision, Recall, and Execution time. It was observed that GPU outperformed CPU in all tests conducted. Moreover, the performance of the neural network models in terms of accuracy increased on GPU compared to CPU.

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Dynamic Resource Allocation-Enabled distributed Learning as a Service for Vehicular Networks

Dynamic Resource Allocation-Enabled Distributed Learning as ...

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Data Science and Information System (ICDSIS), IEEE international conference on

作者： Thirusubramanian Ganesan Ramy Riad Al-Fatlawy Suma Srinath Srinivas Aluvala R. Lakshmana Kumar Cognizant Technology Solutions Texas USA Department of Computers Techniques Engineering College of Technical Engineering The Islamic university Najaf Iraq Department of AIML Nitte Meenakshi Institute of Technology Bengaluru India School of Computer Science and Engineering SR University Warangal Telangana India Department of Computer Science and Engineering (AI & ML) Tagore Institute of Engineering & Technology Salem Tamil Nadu India

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381672

The growth and development of 6 G networks bring a crucial task onto the table which is introducing AI to manage network functions and cope with more users in the future. distributed Learning (DL), as service to technologies, acts as one of the overriding forces motivating the development of 6 G communication system. The 6 G will help in various applications, which are a large set of services; then you will be able to manage the network in a smart and dynamic way. While relatively, many deep learning (DL) methods is deployed within the resource constrained vehicular domain, this setting nonetheless poses significant challenges. The reciprocating development of distributed computing and communication resources, like the edge-cloud continuum and integrated terrestrial-non-terrestrial networks (T/NTN), offers a solution for this. To use these resources and multiple DL methods in an integrated way, the NS becomes the best option. This paper covers the methods of DL that apply best in vehicular environments and examines NS roles alongside these methods, especially in the context of resource management which is dynamic. This research is designed to build a architecture for DL-as-a-Service (DLaaS) letting it to be hosted on a distributed network platform and allowing the implementation of DL algorithms at a proactive basis. This method, in addition to the dynamic resource allocation strategies, permits the efficient control of the different services which have different requirements. Effectiveness of the model is exhibited in a full case step by step in a vehicular T/NTN. The run-down of the DLaaS approach is perceived from its strong points, namely flexibility, performance upgrades, improved network intelligence, and they lead eventually to heightened customer satisfaction from the given cross vehicle or non cross vehicle highway traffic scenarios.

关键词： Computer aided instruction Distance learning Urban areas Signal processing algorithms Signal processing Dynamic scheduling Throughput

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PrioRAT: Criticality-Driven Prioritization Inside the On-Chip Memory Hierarchy 27th

PrioRAT: Criticality-Driven Prioritization Inside the On-Chi...

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

作者： Dimic, Vladimir Moreto, Miquel Casas, Marc Valero, Mateo Barcelona Supercomp Ctr BSC Barcelona Spain Univ Politecn Catalunya UPC Barcelona Spain

ISBN: (纸本)9783030856656;9783030856649

The ever-increasing gap between the processor and main memory speeds requires careful utilization of the limited memory link. This is additionally emphasized for the case of memory-bound applications. Prioritization of memory requests in the memory controller is one of the approaches to improve performance of such codes. However, current designs do not consider high-level information about parallel applications. In this paper, we propose a holistic approach to this problem, where the runtime system-level knowledge is made available in hardware. Processor exploits this information to better prioritize memory requests, while introducing negligible hardware cost. Our design is based on the notion of critical path in the execution of a parallel code. The critical tasks are accelerated by prioritizing their memory requests within the on-chip memory hierarchy. As a result, we reduce the critical path and improve the overall performance up to 1.19 x compared to the baseline systems.

关键词： parallel processing systems

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Real-Time Analytics for Big Data in Smart City applications: Transforming Urban Environments with Data-Driven Insights 3

Real-Time Analytics for Big Data in Smart City Applications:...

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3rd IEEE international conference on Automation and Computation, AUTOCOM 2025

作者： Khanderi, Avinash Rajaram, Gnanajeyaraman Hussein, Ramy Riad Johri, Prashant Mohanraj, T. Balamurugan, M. Walmart Inc. BentonvilleAR United States Simats Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Cse Chennai India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq School of Computing Science and Engineering Galgotias University Greater Noida India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore India Excel Engineering College Department of Eee Komarapalayam India

ISBN: (纸本)9798331542375

This paper offers an overview of real-time analytics for smart cities proposing a framework for real-time data processing to improve the urban landscape. In the proposed system, large-scale data-streams can be processed using advanced machine learning algorithms, edge computing, and distributed data storage. Analyzing the outcomes of the evaluation procedure evidences reportable performance enhancements in the areas of smart city applications. Average traffic speed in transportation systems in the same period rose by 60%, concurrency and response to incidents likewise cut down by 40% and 53% respectively. The energy management systems responded with a 16% decrease in maximum energy load and the waste reduction was by 50%, while the costs savings was by 25% increase. Public safety applications alone realised a 60% decrease in the time taken to detect a crime and a 28.6% increase in efficiency on disaster resources with an overall spike in public satisfaction of 25.7%. Such findings improve the understanding of how this framework can increase urban efficiency, sustainability, and quality of life, which makes the solution usable in future smart city projects. © 2025 IEEE.

关键词： Urban transportation

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