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检索条件"任意字段=International Conference on Advances in Parallel and Distributed Computing"

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A distributed Data Mining and Cloud Analysis for Predictive Gas Level, Dynamic Booking, and Smart Energy Optimization

A Distributed Data Mining and Cloud Analysis for Predictive ...

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2024 international conference on advances in Data Engineering and Intelligent computing Systems, ADICS 2024

作者： Sugumar, D. Ramadevi, R. Prasad, J. Mohankumar, N. Joy Winnie Wise, D.C. Velmurugan, S. Care College Of Arts And Science Department Of Commerce Tamil Nadu Trichy India Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Saveetha University Saveetha Nagar Department Of Biomedical Engineering Tamil Nadu Chennai India Kpr Institute Of Engineering And Technology Department Of Electronics And Communication Engineering Tamil Nadu Coimbatore India B.V. Raju Institute Of Technology Department Of Electronics And Communication Engineering Telangana India Rajalakshmi Institute Of Technology Department Of Computer Science And Engineering Tamil Nadu Chennai India T.J.S. Engineering College Department Of Electronics And Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350364828

The state-of-the-art Internet of Things (IoT) innovation known as the Autonomous Gas Reservoir Management System (AGaRMS) totally changes the most common way of overseeing gas supplies. AGaRMS uses sensors that monitor gas levels and a distributed architecture that forecasts patterns in gas use to ensure that gas refills occur at the right time. Complex AI calculations examine verifiable information, current weather patterns, and usage patterns to estimate when gas will run out. This, then, prompts robotized booking sales to be transported off the supplier. Likewise, AGaRMS appeals to the savvy progression of energy, which empowers strong gas usage. The framework's flexibility and versatility make it reasonable for use in private and business settings, further developing production network productivity, reducing working expenses, and improving the client experience. The AGaRMS system offers a smart method for managing gas leaders that work with gathering effective power energy practices and streamlining gas provisioning processes. AGaRMS optimizes the gas supply chain and improves the user experience by catering to a wide range of residential and industrial applications with its flexible and scalable design. © 2024 IEEE.

关键词： Internet of things

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distributed and Decentralized Edge Caching in 5G Networks Using Non-Volatile Memory Systems 42

Distributed and Decentralized Edge Caching in 5G Networks Us...

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42nd IEEE international conference on distributed computing Systems (ICDCS)

作者： Zeng, Yiming Huang, Yaodong Liu, Zhenhua Liu, Ji SUNY Stony Brook Dept Elect & Comp Engn Stony Brook NY 11794 USA SUNY Stony Brook Dept Appl Math & Stat Stony Brook NY 11794 USA Shenzhen Univ Coll Comp & Software Engn Shenzhen Peoples R China

ISBN: (数字)9781665471770

ISBN: (纸本)9781665471770

Edge caching is an effective way to reduce congestion and latency in 5G networks. Non-volatile memory (NVM) devices are developing fast, with the potential of fast access, and higher endurance versus traditional storage devices, to further boost mobile data offloading efficiency in 5G networks. This paper studies how to effectively use the two-layer storage system (NVM-enhanced) in 5G edge caching. We first model an edge caching optimization problem with NVM storage devices included and develop a parallel distributed algorithm with guaranteed convergence in joint caching and routing decisions. A fully decentralized algorithm for scenarios without any coordination is further developed which also guarantees the convergence. Real-world trace-driven simulations and experiments over a small-scale system demonstrate that NVM significantly boosts the performance of edge caching and the proposed algorithms outperform the existing ones.

关键词： edge caching non-volatile memory distributed and decentralized algorithms 5G networks

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SA_TRSM: A Shape-Aware Auto-Tuning Framework for Small-Scale Irregular-Shaped TRSM 29

SA_TRSM: A Shape-Aware Auto-Tuning Framework for Small-Scale...

引用

29th IEEE international conference on parallel and distributed Systems, ICPADS 2023

作者： Guo, Rongyuan Jia, Haipeng Zhang, Yunquan Deng, Mingsen Wei, Cunyang Chang, Wenbin Zhao, Xiang Guizhou University of Finance and Economics School of Information Beijing China Technology Co. Ltd. Beijing China Chinese Academy of Sciences Institute of Computing Technology Beijing China Ocean University of China College of Computer Science and Technology Beijing China

ISBN: (纸本)9798350330717

TRSM (Triangular Solve with Matrix) is an algorithm in the BLAS library for efficiently solving systems of linear equations, which is widely used in scientific computing, engineering computing, and machine learning. The traditional TRSM algorithm performs well in solving large-scale converging squareshaped matrices but is inefficient in solving small-scale irregularshaped matrices. In this paper, we propose SATRSM, a Shape-Aware auto-tuning framework that is aware of scale size and irregularity, aiming to improve performance on small-scale irregular-shaped TRSM computations. SA TRSM consists of the install-time stage and the run-time stage. In the install-time stage, we designed five components for generating high-performance kernels. In the run-time stage, we designed the Shape-Aware tiling algorithm and Plan Generator for generating an efficient execution plan. The experimental results show that the average performance of SA TRSM in this paper improves by 29.4,16.1,24.6 times, and 7.8 times on double-precision real, single-precision real, doubleprecision complex, and single-precision complex in turn, relative to the algorithms in MKL. © 2023 IEEE.

关键词： assembly optimization BLAS SIMD small-scale and irregularshape TRSM algorithm

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LWSA: A Learning-Based Workflow Scheduling Algorithm for Energy-Efficient UAV Delivery System 29

LWSA: A Learning-Based Workflow Scheduling Algorithm for Ene...

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29th IEEE international conference on parallel and distributed Systems, ICPADS 2023

作者： Wang, Jiali Ye, Yutong Wang, Ting Chen, Mingsong East China Normal University MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application Shanghai China

ISBN: (纸本)9798350330717

Due to their fast speed and easy deployment, Unmanned Aerial Vehicles (UAVs) have been widely used across various sectors, such as earthquake rescue, medical assistance, and smart agriculture. However, UAVs in delivery networks face significant challenges due to limited battery life and computational capabilities, particularly for tasks that entail intensive computing workflows. In this context, Multi-access Edge computing (MEC), which provides computing resources in close proximity to mobile terminal devices, has emerged as a promising solution. UAVs can offload computing tasks to MEC resources across diverse Internet of Things (IoT) environments. Although task offloading can enhance their task processing capability, it simultaneously brings additional costs, encompassing data transmission time and energy consumption. To address these issues, this paper proposes a novel workflow scheduling method based on the Proximal Policy Optimization (PPO) algorithm, aimed at optimizing UAV energy consumption within MEC environments. The proposed approach establishes a learning-based workflow scheduling strategy harnessing the adaptability of the PPO algorithm to manage dynamic and intricate scenarios, which facilitates efficient task allocation to optimal computational resources while accounting for flight time constraints. Extensive experiments conducted on various well-known scientific workflow benchmarks in real-world UAV delivery networks validate the effectiveness of our method. Compared with state-of-the-art methods, our approach significantly reduces UAV energy consumption and task completion time, simultaneously increasing UAV's effective payload capacity. © 2023 IEEE.

关键词： Energy Efficiency Multi-Access Edge computing Reinforcement Learning Workflow Scheduling

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A Novel Robust Reinforcement Learning-based Dependent Task Offloading Algorithm for Mobile Edge Intelligence 29

A Novel Robust Reinforcement Learning-based Dependent Task O...

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29th IEEE international conference on parallel and distributed Systems, ICPADS 2023

作者： Deng, Xu Sun, Peng Yang, Kun Fang, Gaoyun Boukerche, Azzedine Song, Liang Fudan University Academy for Engineering and Technology China Innovation Platform for Academicians of Hainan Province Haikou570228 China Duke Kunshan University China University of Ottawa Canada

ISBN: (纸本)9798350330717

With the rise of advanced applications based on Artificial Intelligence (AI) and Internet-of-Things (IoT), mobile devices have become more intelligent, introducing a novel concept, Mobile Edge Intelligence. But the limited on-board resources often hinder the capabilities of mobile devices. Mobile Edge computing (MEC), regarded as an effective method to expand device capability, effectively overcomes this barrier. However, the dynamic networks driven by mobility and the dependency on applications pose significant challenges for offloading, which can degrade MEC's overall performance. Therefore, how to effectively combine the above points to achieve a stable and effective sharing of computing resources between devices and servers is a critical issue. In this paper, we consider a multi-slot MEC system with device mobility and multiple applications of unknown arrival. To improve application completion rate while reducing task delay, we introduce a novel, robust distributed offloading algorithm, which calls the Multi-Attention Pointer network-based Reinforcement Learning algorithm (MAPRL), for the dynamic and unstable resource offloading scenario. Numerous experiments have been carried out to demonstrate that, compared with the existing methods, MAPRL exhibits robustness when facing the changing scenario, it can adapt to the unknown workload and dynamic network connections to enhance the offloading performance. © 2023 IEEE.

关键词： deep reinforcement learning dependent task offloading Mobile edge computing

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RAPIDS: Reconciling Availability, Accuracy, and Performance in Managing Geo-distributed Scientific Data 23

RAPIDS: Reconciling Availability, Accuracy, and Performance ...

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32nd international Symposium on High-Performance parallel and distributed computing (HPDC) part of the ACM Federated computing Research conference (FCRC)

作者： Wan, Lipeng Chen, Jieyang Liang, Xin Gainaru, Ana Gong, Qian Liu, Qing Whitney, Ben Arulraj, Joy Liu, Zhengchun Foster, Ian Klasky, Scott Georgia State Univ Dept Comp Sci Atlanta GA 30303 USA Univ Alabama Birmingham Dept Comp Sci Birmingham AL USA Univ Kentucky Dept Comp Sci Lexington KY USA Oak Ridge Natl Lab Oak Ridge TN USA New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA Georgia Inst Technol Sch Comp Sci Atlanta GA 30332 USA Argonne Natl Lab Lemont IL USA

ISBN: (纸本)9798400701559

In modern science, big data plays an increasingly important role. Many scientific applications, such as running simulations on supercomputers or conducting experiments on advanced instruments, produce huge amount of data at unprecedented speed. Analyzing and understanding such big data is the key for scientists to make scientific breakthroughs. However, data might become unavailable for scientists to access when outages or maintenance of the storage system occur, which severely hinders scientific discovery. To improve the data availability, data duplication and erasure coding (EC) are often used. But as the scientific data gets larger, using these two methods can cause considerable storage and network overhead. In this paper, we propose RAPIDS, a hybrid approach that combines the multigrid-based error-bounded lossy compression with erasure coding, to significantly reduce the storage and network overhead required for maintaining high data availability. Our experiments show that RAPIDS reduces the storage overhead by up to 7.5x and network overhead by up to 3x to achieve the same level of availability compared to the regular EC method. We improve RAPIDS by building two models to optimize the fault tolerance configurations and data gathering strategy. We demonstrate that RAPIDS significantly improves performance when running on many CPU cores in parallel or on GPUs.

关键词： scientific data Management data availability

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Performance, Energy and parallelism: Using Near Data Processing in Utility and Cloud computing 15

Performance, Energy and Parallelism: Using Near Data Process...

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IEEE/ACM 15th international conference on Utility and Cloud computing (UCC) / 9th international conference on Big Data computing, Applications and Technologies (BDCAT)

作者： Agha, Gul Mukherjee, Dipayan Sandur, Atul Univ Illinois Dept Comp Sci Urbana IL 61801 USA

ISBN: (纸本)9781665460873

Massive amounts of data are generated by sensor networks, edge computers, IoT devices, and enterprise networks. To process this volume of data requires (1) a scalable programming model that is not only concurrent and distributed, but supports the mobility of data and processes (actors), and (2) algorithms to distribute computations between nodes in a manner that improves overall performance while considering energy use in the system. With appropriate programming tools, we can distribute a given computation in a way that makes effective use of edge devices to improve performance while lowering energy consumption. The paper describes our work building on ideas based on the Actor model of computation. These include characterizing the relation of performance and energy consumption in parallel computation, and methods to support scalable placement mechanisms under dynamically changing network conditions and computational loads on edge devices. The paper will conclude with a presentation with a summary of open research problems.

关键词： edge computing actor model energy efficiency distributed computation

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parallel Directives Evaluation in Porous Media Application: A Case Study 31

Parallel Directives Evaluation in Porous Media Application: ...

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31st Euromicro international conference on parallel, distributed and Network-Based Processing, PDP 2023

作者： Lucca, Natiele Schepke, Claudio Tremarin, Gabriel Alegrete Brazil

ISBN: (纸本)9798350337631

High-performance computing provides the acceleration of scientific applications through the use of parallelism. Applications of this type usually demand a lot of computation time for a version with a single code execution stream. The adoption of different models of parallel programming enables the development of concurrent code. In this sense, this paper evaluates parallel interfaces and their programming models. Therefore, as a case study, we evaluate a porous media application that simulates grain drying using OpenMP (loop, sections, tasks, target, and teams approach) and OpenACC programming interfaces. The results show a reduction in processing time in all test cases. The total parallel simulation time for a multicore architecture using 16 physical cores was 5.61 times less using loops, 5.96 using targets, and 7.50 using teams. Task and section directives produce around 1.20 speedup due to the limitations of concurrent task executions of the application. The reduction using a single GPU was 7.54. We also contribute with some collected traces, identifying the parallel steps and synchronization time. © 2023 IEEE.

关键词： Porous materials

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parallel Cross Strip Attention Network for Single Image Dehazing 24

Parallel Cross Strip Attention Network for Single Image Deha...

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5th international conference on computing, Networks and Internet of Things (CNIOT)

作者： Tong, Lihan Liu, Yun Ye, Tian Li, WeiJia Chen, Liyuan Chen, Erkang Jimei Univ Xiamen Peoples R China Southwest Univ Chongqing Peoples R China Nanyang Technol Univ Singapore Singapore Hong Kong Univ Sci & Technol Guangzhou Guangzhou Peoples R China

ISBN: (纸本)9798400716751

The objective of single image dehazing is to restore hazy images and produce clear, high-quality visuals. Traditional convolutional models struggle with long-range dependencies due to their limited receptive field size. While Transformers excel at capturing such dependencies, their quadratic computational complexity in relation to feature map resolution makes them less suitable for pixel-to-pixel dense prediction tasks. Moreover, fixed kernels or tokens in most models do not adapt well to varying blur sizes, resulting in suboptimal dehazing performance. In this study, we introduce a novel dehazing network based on parallel Stripe Cross Attention (PCSA) with a multi-scale strategy. PCSA efficiently integrates long-range dependencies by simultaneously capturing horizontal and vertical relationships, allowing each pixel to capture contextual cues from an expanded spatial domain. To handle different sizes and shapes of blurs flexibly, We employs a channel-wise design with varying convolutional kernel sizes and strip lengths in each PCSA to capture context information at different scales. Additionally, we incorporate a softmax-based adaptive weighting mechanism within PCSA to prioritize and leverage more critical features. We perform extensive testing on synthetic and real-world datasets and our PCSA-Net has established a new performance benchmark. This work not only advances the field of image dehazing but also serves as a reference for developing more diverse and efficient attention mechanisms. CCS Concepts: center dot Theory of computation. Theory and algorithms for application domains. Machine learning theory. Models of learning

关键词： parallel Cross Strip Attention Single Image Dehazing Horizontal Strip Attention Vertical Strip Attention

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Didal: distributed Data Library for Development of parallel Fragmented Programs 17th

Didal: Distributed Data Library for Development of Paralle...

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parallel computing Technologies - 17th international conference, PaCT 2023, Proceedings

作者： Malyshkin, Victor Schukin, Georgy Institute of Computational Mathematics and Mathematical Geophysics SB RAS Novosibirsk Russia Novosibirsk State Technical University Novosibirsk Russia Novosibirsk State University Novosibirsk Russia

ISBN: (纸本)9783031416729

Nowadays with rapid evolution of high-performance computing systems it’s becoming essential to have tools to simplify development of efficient portable parallel programs for these systems. Fragmented programming is a technology where parallel program is represented as a collection of pieces of data (data fragments) and computations on these pieces (computation fragments), able to be tuned to the resources of a computing system and automatically provide such facilities as dynamic load balancing. Didal is a distributed data library to support development of efficient parallel fragmented programs on distributed memory supercomputers. The library contains facilities for data partitioning, distribution and load balancing. In this paper foundations of the library are explained and applicability of the library is demonstrated with Particle-in-Cell (PIC) method implementation, which shows performance comparable to conventional parallel programming tools. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： parallel programming

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