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检索条件"任意字段=3rd International Conference on Distributed Computing and High Performance Computing, DCHPC 2024"

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2024 3rd international conference on distributed computing and high performance computing, dchpc 2024

2024 3rd International Conference on Distributed Computing a...

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3rd international conference on distributed computing and high performance computing, dchpc 2024

ISBN: (纸本)9798350381580

The proceedings contain 21 papers. The topics discussed include: enhancing the performance of photonic crystal and gates with machine learning optimization;bitcoin price prediction based on financial data, technical indicators, and news headlines sentiment analysis using CNN and GRU deep learning algorithms;promoting cybersecurity knowledge via gamification: an innovative intervention design;modeling and verification of the causal broadcast algorithm using colored Petri Nets;GPU-based parallel technique for solving n-similarity problem in textual data mining;a novel approach for specification and verification of symmetric distributed algorithms using spin;optimizing geophysical workloads in high-performance computing: leveraging machine learning and transformer models for enhanced parallelism and processor allocation;and rational Jacobi kernel functions: a novel massively parallelizable orthogonal kernel for support vector machines.

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FRAME 2024 - Proceedings of the 2024 on Workshop on Flexible Resource and Application Management on the Edge, Part of: HPDC 2024 - 3rd international Symposium on high-performance Parallel and distributed computing

FRAME 2024 - Proceedings of the 2024 on Workshop on Flexible...

引用

4th Workshop on Flexible Resource and Application Management on the Edge, FRAME 2024

ISBN: (纸本)9798400706417

The proceedings contain 9 papers. The topics discussed include: towards sustainable deployment of microservices over the Cloud-IoT continuum, with FREEDA;towards enabling observability of energy demand, with NEST;urgent edge computing;optimizing resource allocation in the edge: a minimum weighted vertex cover approach;towards energy-aware execution and offloading of serverless functions;reactive autoscaling of Kubernetes nodes;OUTFIT: crowdsourced data feeding noise maps in digital twins;encoding consistency: optimizing self-driving reliability with real-time speed data;and striking trade-off between high performance and energy efficiency in an edge computing application for detecting floating plastic debris.

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Proceedings of IPDRM 2019: 3rd Annual Workshop on Emerging Parallel and distributed Runtime Systems and Middleware - Held in conjunction with SC 2019: The international conference for high performance computing, Networking, Storage and Analysis

Proceedings of IPDRM 2019: 3rd Annual Workshop on Emerging P...

引用

3rd IEEE/ACM Annual Workshop on Emerging Parallel and distributed Runtime Systems and Middleware, IPDRM 2019

ISBN: (纸本)9781728159935

The proceedings contain 6 papers. The topics discussed include: assessing the performance impact of using an active global address space in HPX: a case for AGAS;sequential codelet model of program execution - a super-codelet model based on the hierarchical turing machine;ADVERT: an asynchronous runtime for fine-grained network systems;design and evaluation of shared memory communication benchmarks on emerging architectures using MVAPICH2;characterizing the performance of executing many-tasks on summit;and leveraging network-level parallelism with multiple process-endpoints for MPI broadcast.

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A Novel Approach for Specification and Verification of Symmetric distributed Algorithms Using SPIN 3

A Novel Approach for Specification and Verification of Symme...

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3rd international conference on distributed computing and high performance computing, dchpc 2024

作者： Namvaritazehkand, Leila Ebnenasir, Ali University of Tabriz Faculty of Electrical and Computer Engineering Department of Computer Engineering East Azerbaijan Tabriz Iran Michigan Technological University Department of Computer Science HoughtonMI49931 United States

ISBN: (纸本)9798350381580

This paper focuses on the challenges of modeling and verifying symmetric distributed algorithms with point-to-point and bidirectional communications using the SPIN model checker. In this paper, we first state the problems of implementing the model of such algorithms in the Promela language. Then, we present a solution for each of these problems, where these solutions present a hybrid data structure that facilitates the definition of any set of variables or channels as a matrix. Also, we model and verify one of the valuable and practical algorithms called the causal broadcast (CB) algorithm as a case study using the proposed method. In addition, we specify the main properties of the CB algorithm, namely validity, integrity, causal delivery, and termination, in Linear Temporal Logic (LTL) and then verify them using the SPIN model checker. © 2024 IEEE.

关键词： Model checking

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An Adaptive Task Scheduling Approach for Cloud computing Using Deep Reinforcement Learning 3

An Adaptive Task Scheduling Approach for Cloud Computing Usi...

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3rd international conference on distributed computing and high performance computing, dchpc 2024

作者： Amini, Pedram Kalbasi, Amir Amirkabir University of Technology Department of Computer Engineering Tehran Iran

ISBN: (纸本)9798350381580

Today, many important internet services are provided on cloud computing platforms. The ever-increasing expansion of services and user requirements has necessitated the optimal use of resources. Therefore, several algorithms for optimal resource usage have been proposed to increase cloud performance and provide more satisfactory services to internet users. Typically, each algorithm is suitable for a specific environment, and their performance is affected by changes of the execution environment. Meanwhile, the assignment of tasks to resources in cloud computing is a difficult problem that NP-Complete. This paper proposes a novel deep reinforcement learning-based approach for task scheduling in cloud computing environments, aiming to minimize the makespan, which is the total time required to complete all tasks. The proposed approach utilizes a deep Q-learning algorithm to learn the near-optimal task allocation strategies based on the current state of the system. The task scheduling performance of the proposed approach is compared with the Min-Min, Min-Max, FCFS and GA algorithms based on three criteria: makespan, algorithm execution time, and computational complexity. Simulation results demonstrate that the proposed approach results in excellent makespan, while demonstrating very small algorithm execution time. © 2024 IEEE.

关键词： Reinforcement learning

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A Blockchain Mining Proof of Work Approach Based on Fog computing Virtualization for Mobile CrowdSensing 3

A Blockchain Mining Proof of Work Approach Based on Fog Comp...

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3rd international conference on distributed computing and high performance computing, dchpc 2024

作者： Mahdi, Abbas Fadhil Rabee, Furkan University of Kufa Faculty of Computer Science and Mathematics Department of Computer Science Najaf Iraq

ISBN: (纸本)9798350381580

The most important and common consensus mechanism in the blockchain is the Proof-of-Work (PoW) algorithm, which requires a large amount of energy and consumes more time from the miners to reach a single distributed agreement by solving the hard puzzle to append a new block to the blockchain, in addition to the low throughput of accomplishing transactions. The paper proposed a parallel mechanism to implement the proof of work to increase the speed of the mining process by splitting the encryption hash range SHA256 (2256) into numerous subranges through utilizing the mobile crowdsensing (MCS) environment and virtualization in fog computing (FC). The fog virtualization-based Proof of Work (FV-PoW) method outperformed other methods and was very productive in the mining process, especially at targets with higher difficulty. To test this, the experimental work was done by making the mining difficulty 9 levels higher to find a difficult target. The mining process took a time of 81.6 sec at level 8 in comparison to the conventional approach, which consumed 6221 sec. At level 9, the solution was not traditionally obtained. Conversely, the suggested approach was able to find the solution within 2043.1 sec. This solution guarantees substantial scalability. © 2024 IEEE.

关键词： Blockchain

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Optimizing Geophysical Workloads in high-performance computing: Leveraging Machine Learning and Transformer Models for Enhanced Parallelism and Processor Allocation 3

Optimizing Geophysical Workloads in High-Performance Computi...

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3rd international conference on distributed computing and high performance computing, dchpc 2024

作者： Wyzykowski, Andre Brasil Vieira Sousa, Gabriel Mascarenhas Costa De Coelho, Breno Spinelli Santos, Lucas Batista Anschau, Darlan High Performance Computing Center Senai Cimatec Salvador Brazil

ISBN: (纸本)9798350381580

high-performance computing (HPC) is essential in processing complex models, a necessity particularly emphasized in geophysics imaging. Its superiority over personal computing lies in its ability to implement parallelism by distributing workloads across supercomputer clusters. However, users often face challenges in selecting the most effective machine configuration for their specific computational tasks, which is crucial for optimizing speed and efficiency. This paper addresses this issue and presents a machine learning-based toolkit specifically tailored for geophysics workloads as a solution. The focus in this domain ensures that the toolkit's recommendations are highly relevant for applications within this field. The toolkit pre-emptively evaluates various machine configurations, offering users personalized recommendations for their geophysics computational tasks. Significant results from our processor classifier network demonstrate the toolkit's efficacy. It achieved an overall accuracy of 70%, with mean precision, recall, and F1-score of 0.73, 0.70, and 0.64, respectively. A notable performance was observed in processors like the AMD EPYC 7773X 64-Core and various Intel(R) Xeon(R) models, some achieving perfect precision and recall scores. This toolkit, with its focus on geophysics workloads, empowers users to make informed decisions, greatly enhancing the efficiency of selecting optimal HPC configurations for specific needs in geophysics imaging and related applications. © 2024 IEEE.

关键词： Geophysics

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GPU-based Parallel Technique for Solving the N-Similarity Problem in Textual Data Mining 3

GPU-based Parallel Technique for Solving the N-Similarity Pr...

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3rd international conference on distributed computing and high performance computing, dchpc 2024

作者： Fazlali, Mahmood Mirhosseini, Mina Shahsavari, Mahyar Shafarenko, Alex Mashinchi, Mashallah University of Hertfordshire Cyber Security and Computing Research Group Department of Computer Science Hertfordshire United Kingdom Shahid Beheshti University Faculty of Mathematical Sciences Department of Computer and Data Sciences Tehran Iran Radboud University Donders Institute for Brain Cognition and Behaviour Ai Department Nijmegen Netherlands Shahid Bahonar University of Kerman Faculty of Mathematics and Computer Department of Statistics Kerman Iran

ISBN: (纸本)9798350381580

An important issue in data mining and information retrieval is the problem of multiple similarity or n-similarity. This problem entails finding a group of n data points with the highest similarity within a large dataset. Exact methods to solve this problem exist but come with high time and space complexities. Additionally, various metaheuristic algorithms have been proposed, including genetic algorithms, gravitational search algorithms, particle swarm optimization, imperialist competitive algorithms, and fuzzy imperialist competitive algorithms. These metaheuristics are capable of finding near-optimal solutions within a reasonable timeframe, although there is no guarantee of achieving exact results. In this paper, we employ a parallelization technique using CUDA to expedite the exact method. We conduct experiments on textual datasets to identify a group of n textual documents with the highest similarity to each other. The experimental results demonstrate that the proposed parallel exact method significantly reduces execution time compared to the best sequential approach and CPU multi-core implementation. Furthermore, it is evident that the proposed method requires less memory space than the exact method. © 2024 IEEE.

关键词： Large datasets

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Bitcoin price prediction based on financial data, technical indicators, and news headlines sentiment analysis using CNN and GRU deep learning algorithms 3

Bitcoin price prediction based on financial data, technical ...

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3rd international conference on distributed computing and high performance computing, dchpc 2024

作者： Arjmand, Masoud Kazeminia, Saman Sajedi, Hedieh University of Tehran Kish International Campus Department of Computer Engineering Kish Iran NHL Stenden University of Applied Science Department of Computer Vision and Data Science Leeuwarden Netherlands University of Tehran Department of Mathematics Statistics and Computer Science Tehran Iran

ISBN: (纸本)9798350381580

Bitcoin is the leading cryptocurrency with the highest market value among digital currencies. Therefore, predicting the value of Bitcoin can help to understand the entire cryptocurrency market. However, Bitcoin has had a lot of price fluctuations since its inception. In this paper, we are going to forecast the price of Bitcoin using news headline analysis, technical analysis indicators, and historical financial data. The news headlines used in this article are scraped from the Cointelegraph news website, which contains 3988 news headlines related to Bitcoin between 2/7/2020 and 3/8/2021. A transformer pre-trained model on cryptocurrency-related texts called CryptoBERT, which is a BERT-based sentiment analysis model, has been used to analyze the textual data. Also, a novel hybrid 2DCNN-GRU deep learning model has been used to predict the price. To adjust the parameters of this model, a parameter tuning method based on orthogonal arrays called the Taguchi method has been employed. Finally, to examine the proposed model's efficiency, the obtained results have been compared with other deep learning models from the literature review that used text data to predict bitcoin prices. The results show that this model outperformed other models in terms of MAE criterion, while in the other three criteria, namely MSE, RMSE and MAPE, it still demonstrated good results. © 2024 IEEE.

关键词： Forecasting

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An Edge computing Framework for Data Analysis in Intelligent Transportation Systems 3

An Edge Computing Framework for Data Analysis in Intelligent...

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3rd international conference for Innovation in Technology, INOCON 2024

作者： Sharma, Sumit Bali, Rasmeet Singh Chandigarh University Computer Science Engineering Punjab India

ISBN: (纸本)9798350381931

Intelligent Transportation Systems (ITS) have been identified as a key application of the Internet of Things (IoT). In order to reduce latency and bandwidth needs, edge computing offers a distributed computing architecture that can process and analyse data closer to the source. In this paper, we propose an edge computing framework for data analysis in ITS and present experimental results to evaluate its performance. The proposed framework aims to address the challenges associated with traditional data analysis techniques by leveraging the power of distributed computing resources, advanced analytics, and real-time processing capabilities. The rationale behind the framework's design and architecture is to improve the efficiency, scalability, and adaptability of ITS solutions, ensuring faster decision-making, reduced latency, and enhanced overall transportation performance. © 2024 IEEE.

关键词： Edge computing

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