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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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Collaborative Scheduling of Multi-cloud distributed Multi-cloud Tasks Based on Evolutionary Multi-tasking Algorithm 18th

Collaborative Scheduling of Multi-cloud Distributed Multi-cl...

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18th international conference on Bio-Inspired computing: Theories and Applications (BIC-TA)

作者： Zhao, Tianhao Wu, Linjie Cui, Zhihua Cai, Xingjuan Taiyuan Univ Sci & Technol Shanxi Lab Big Data Anal & Parallel Comp Taiyuan 030024 Peoples R China

ISBN: (纸本)9789819722716;9789819722723

With the rapid development of information technology making the scale of the Internet increasing day by day, collaborative optimization of multiple scheduling tasks in a multi-cloud environment provides users with faster scheduling options. Meanwhile, there is a certain similarity between cloud scheduling tasks, and in order not to waste the similarity between tasks, similar tasks are linked together to find an optimal scheduling solution for multiple tasks, making it possible to handle multiple scheduling tasks simultaneously. Firstly, we construct a multi-objective optimization model considering time, cost and VM resource load balance;secondly, since there are not only independent optimization problems in real scenarios, we adapt the constructed multiple similar optimization models and propose a multi-task multi-objective optimization model;finally, to be able to solve the constructed model better, we use a proposed objective function-based Finally, we propose an evolutionary multitasking algorithm based on weighted summation of the objective functions, which allows the algorithm to find the optimal solution among multiple multi-objective models. Simulation experiments show that the proposed algorithm has better performance.

关键词： Evolutionary Multitasking algorithm Task scheduling Cloud computing Optimization

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CoCV: Heterogeneous Processors Collaboration Mechanism for End-to-End Execution of Intelligent Computer Vision Tasks on Mobile Devices 29

CoCV: Heterogeneous Processors Collaboration Mechanism for E...

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

作者： Wan, Ye Liu, Mengyang Li, Guangtong Dong, Fang Southeast University School of Computer Science and Engineering Nanjing China

ISBN: (纸本)9798350330717

Object detection, image classification, and various other computer vision tasks have become prevalent on mobile devices. These computer vision tasks are typically executed with three stages: pre-processing, inference, and post-processing. Mobile SoC, serving as the computing unit on mobile devices, typically consists of heterogeneous processors like CPU, GPU, and NPU. However, during the execution of a computer vision task, current available frameworks only achieve the parallelism of CPU and GPU in the inference stage. While during pre- and post-processing, only CPU is used, leaving GPU and NPU on the SoC to be idle. For mobile applications that require low latency, the overhead of pre-processing and post-processing stages often account for more than 50% of the total latency, which becoming a performance bottleneck of the entire task. To reduce latency, it is imperative to fully utilize the idle heterogeneous processors (GPU, NPU) on the SoC and achieve heterogeneous processors parallelism in all three stages during execution. In this paper, we propose CoCV, a heterogeneous processor parallel computing system for computer vision tasks on mobile devices. In CoCV, we are the first to build an image processing operator library for heterogeneous parallel computing on mobile devices. Besides, we design a task allocation scheduling algorithm to guide the partitioning of processing tasks during execution, which ensuring a relatively balanced workload between different processors. A cross-stage operator chaining technique is also proposed to reduce the data sharing overhead among different processors during execution. We build a prototype system and evaluate it with different computer vision tasks. The results show up to 33% latency reduction for end-to-end tasks and 2.32× speedup compared with the current best solution. © 2023 IEEE.

关键词： CPU&GPU Co-execution Heterogeneous computing Mobile devices

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Research on Application of Optimized Hybrid Mobile Edge computing Algorithm in Workflow Task Scheduling

Research on Application of Optimized Hybrid Mobile Edge Comp...

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international conference on Wireless Communications, Networking and Applications (WCNA 2022)

作者： Liu, Yuehua Ding, Jiafu School of Computer Science Hunan University of Technology and Business Changsha410205 China

ISBN: (纸本)9789819939503

In the Mobile Edge computing environment, the calculation of data relies on the heterogeneous computing system in the distributed environment, which can effectively execute parallel and distributed computing. The DAG task model is a classic task model in a computing system. In this mode, the execution of some tasks has data dependencies and strict execution order requirements. This paper studies the DAG task model and proposes a task scheduling algorithm based on genetic algorithm and improved particle swarm optimization algorithm. The data transmission time and execution time that affect the scheduling are considered evaluate the quality of the scheme. The result calculated by genetic algorithm is used as the initial value of the particle swarm, and the inertia weight and learning factor of the particle swarm are adaptively adjusted to improve the quality of the final scheme. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Genetic algorithms

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Enhancing Performance in Heterogeneous computing: A Comparative Study of CUDA on GPUs and CPUs 5

Enhancing Performance in Heterogeneous Computing: A Comparat...

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5th IEEE international conference on advances in Electronics, Computers and Communications, ICAECC 2023

作者： Takodara, Mitul N. Parmar, Parth Ravikumar, R.N. Sivakumar, N. Tiwari, Rohit Kumar Marwadi University Dept. of Computer Engineering Rajkot India Banglore India School of Computer Engineering KIIT Deemed to Be University Bhubaneswar India

ISBN: (纸本)9798350305449

Heterogeneous computing, in which Graphics Processing Units (GPUs) serve as coprocessors to CPUs for arithmetic-intensive data-parallel tasks, has become increasingly popular due to the pressing demand for higher computational efficiency in science and engineering. When it comes to heterogeneous computing on NVIDIA GPUs, the emerging standard is CUDA, or Compute Unified Device Architecture. The primary objective of CUDA is to free developers from the burden of implementing parallel algorithms and instead allow them to concentrate on the work at hand. The main objective of the work is to obtain the performance gain in execution speed for the dynamic algorithms which generally are complex and takes a very long time for execution and compare results on different GPU processors and CPU and have a comparative study of algorithms. This work is mainly concentrated on working with dynamic algorithms such as Knapsack, Binary Search, Longest Common Subsequence, Kruskal's Algorithm on GTX 480 and Tesla C2070 GPUs. Also, the work consists of preliminary sorting algorithms such as Insertion Sort, Selection Sort, and Bubble Sort. Report contains study of the NVIDIA CUDA architecture, CUDA SDK tool kit and different methods to get performance benefit, implementation of intermediate tool to find out functions and their dependencies from the source code and the implementation of the complete algorithm, testing and obtaining statistics for the same. The performance of such algorithms was improved by a factor of 2X to 64X when implemented on GPU as opposed to CPU. © 2023 IEEE.

关键词： Graphics processing unit

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REVENUE FORECASTING USING distributed MACHINE LEARNING IN ONLINE ADVERTISING SYSTEMS

REVENUE FORECASTING USING DISTRIBUTED MACHINE LEARNING IN ON...

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2023 IEEE international conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering, RMKMATE 2023

作者： Murugan, P. Esakkiammal, S. Kanagavalli, N. Priya, P. Krishna Martis, Jason Elroy Kavitha, P. Pallavaram Tamilnadu Chennai India Department of Library Central University of Tamil Nadu Neelakudi Tamilnadu Thiruvarur India Department of CSE Rajalakshmi Institute of Technology Kuthambakkam Tamilnadu Chennai India KL Business School KL Education Foundation Guntur Andrha Pradesh Vaddeswaram India Department of Information Science and Engineering NMAM Institute of Technology Nitte Deemed to be University Karnataka Karkala India Department of Computer Science and Engineering R.M.K. Engineering College Tamilnadu India

ISBN: (纸本)9798350305708

This research delves into the utilization of distributed machine learning methods for revenue forecasting in the realm of online advertising systems. As the domain of digital advertising continues to expand rapidly, the accurate prediction of revenue becomes indispensable for optimizing resource allocation and making well-informed decisions. Conventional approaches frequently encounter difficulties in managing the sheer volume and intricacy of advertising data. In contrast, distributed machine learning harnesses the capabilities of parallel processing and distributed computing to efficiently process and analyze large datasets. This investigation explores diverse distributed machine learning algorithms, including gradient boosting, random forests, and neural networks, within the context of revenue forecasting. The research assesses their performance, scalability, and precision in comparison to traditional techniques. Through empirical experiments and case studies, we showcase the potential of distributed machine learning to elevate the accuracy of revenue forecasting in online advertising systems, ultimately contributing to more informed business strategies and enhanced revenue generation. © 2023 IEEE.

关键词： algorithm scalability distributed machine learning online advertising systems parallel processing revenue forecasting

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GPU-Accelerated Tree-Search in Chapel versus CUDA and HIP

GPU-Accelerated Tree-Search in Chapel versus CUDA and HIP

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1st international conference on Smart Energy Systems and Artificial Intelligence (SESAI)

作者： Helbecque, Guillaume Krishnasamy, Ezhilmathi Melab, Nouredine Bouvry, Pascal Univ Luxembourg DCS FSTM SnT Luxembourg Luxembourg Univ Lille CNRS CRIStAL UMR 9189 Ctr Inria Lille France

ISBN: (纸本)9798350364613;9798350364606

In the context of exascale programming, the PGAS-based Chapel is among the rare languages targeting the holistic handling of high-performance computing issues including the productivity-aware harnessing of Nvidia and AMD GPUs. In this paper, we propose a pioneering proof-of-concept dealing with this latter issue in the context of tree-based exact optimization. Actually, we revisit the design and implementation of a generic multi-pool GPU-accelerated tree-search algorithm using Chapel. This algorithm is instantiated on the backtracking method and experimented on the N-Queens problem. For performance evaluation, the Chapel-based approach is compared to Nvidia CUDA and AMD HIP low-level counterparts. The reported results show that in a single-GPU setting, the high GPU abstraction of Chapel results in a loss of only 8% (resp. 16%) compared to CUDA (resp. HIP). In a multi-GPU setting, up to 80% (resp. 71%) of the baseline speed-up is achieved for coarse-grained problem instances on Nvidia (resp. AMD) GPUs.

关键词： Chapel Tree-Search GPU computing CUDA HIP N-Queens AMD Nvidia

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AFL-CS: Asynchronous Federated Learning with Cosine Similarity-based Penalty Term and Aggregation 29

AFL-CS: Asynchronous Federated Learning with Cosine Similari...

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

作者： Yan, Bingjie Jiang, Xinlong Chen, Yiqiang Gao, Chenlong Liu, Xuequn Chinese Academy of Sciences Institute of Computing Technology Beijing China Beijing Key Laboratory of Mobile Computing and Pervasive Device Beijing China University of Chinese Academy of Sciences Beijing China Nanchang Aier Eye Hospital Nanchang China

ISBN: (纸本)9798350330717

Horizontal Federated Learning offers a means to develop machine learning models in the realm of medical application while preserving the confidentiality and security of patient data. However, due to the substantial heterogeneity of the devices in medical institution, traditional synchronous federated aggregation methods result in a noticeable decrease in training efficiency, thereby impacting the application and deployment of federated learning. Asynchronous Federated Learning (AFL) model aggregation methods can mitigate this problem but present new challenges in terms of convergence stability and speed. In this paper, we propose a cosine similarity-based layer-wise penalty term and asynchronous model aggregation method AFL-CS, which considers the global model convergence direction during local training. Compared with existing AFL aggregation methods, AFL-CS can achieve faster and more consistent convergence direction to superior performance especially in non-iid settings with high statistical heterogeneity, even reaching and exceeding synchronous FL. © 2023 IEEE.

关键词： asynchronous federated learning medical application model aggregation privacy protection

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Highly Accurate and Energy Efficient Stochastic Multipliers 22

Highly Accurate and Energy Efficient Stochastic Multipliers

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IEEE 22nd international conference on Nanotechnology (NANO)

作者： Zhang, Yongqiang Xie, Lingyun Han, Jie Xie, Guangjun Hefei Univ Technol Sch Microelect Hefei 230009 Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G IH9 Canada

ISBN: (数字)9781665452250

ISBN: (纸本)9781665452250

Stochastic computing (SC) has gained interest by virtue of its small area and low power in computing units through encoding numerical values into randomly distributed bitstreams. However, this method not only results in long latency and thus increases energy consumption, but also reduces computing accuracy. Fortunately, stochastic bitstreams can be generated in parallel to reduce the latency. In this paper, parallel and serial multipliers using optimized encoding sequences with high accuracy and low energy consumption are proposed. Experimental results show that the mean squared error (MSE) is only 0.0015 for a 3-bit serial/parallel multiplier. The MSEs of 3-to 8-bit multipliers are reduced by approximately 29% on average compared with existing designs. The serial multiplier reduces the area and energy consumption by approximately 28% and 24% on average. The parallel multiplier reduces the latency and energy consumption by about 78% and 57% on average, respectively. To evaluate the effectiveness of the proposed multipliers, an image multiplication algorithm is implemented. The results show that the proposed multipliers yield higher image qualities than previous designs.

关键词： Stochastic computing thermometer code multiplier image multiplication

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Resource Allocation and Optimization Scheduling Scheme of Edge Resources in Fog computing Access Network

Resource Allocation and Optimization Scheduling Scheme of Ed...

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international conference on advances in Data Science and computing Technologies, ASDC 2022

作者： Maram, Balajee Daniya, T. Gampala, Veerraju Apex Institute of Technology-CSE Chandigarh University Gharuan-Mohali India Department of CSE-AI&ML GMR Institute of Technology Rajam India Department of CSE Koneru Lakshmaiah Edu. Foundation Andhra Pradesh Guntur Vaddeswaram India

ISBN: (纸本)9789819936557

Due to the limitations of cloud computing and rise of Internet of Things (IoT) promotes a novel distributed computing approach named fog computing, that can processing the data at network edge. The fog computing is a scalable, distributed computing infrastructure that transfer computations tasks closer to smart gateways or edge devices. The resource allocation scheduling optimization schemes are used for minimizing the system total delay. In fog computing, optimization plays a vital role. For instance, reducing consumption of energy and latency is only as significant as maximizing reliability and security. Therefore, resource allocation and optimization scheduling scheme of edge resources in fog computing access network architecture described in this work. In fog computing access system, the optimization is investigated between processing delay and transmission delay. The results of simulation exhibited analysis correctness by reducing the average delay of system tasks. Therefore, the performance of fog computing access networks is improved. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fog computing

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A Comprehensive Survey on Security of Single Source Cloud to distributed Enviroments Edge and Fog computing 1

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international conference on Power Engineering and Intelligent Systems, PEIS 2023

作者： Yadav, Dushyant Kumar Sahu, Hemlal Saradhi, Tejendra Dhanvinesh, K. Vishnubalaji, Loganatha Rana, Saurabh Chaudhary, Dharminder Department of Mathematics J.Y. Govt. College Raipur India Bennett University Noida India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9789819973835

ISBN: (纸本)9789819973828

The study of information security and privacy is currently quite popular. In parallel, several computing paradigms, such as cloud and edge computing, are already creating a unique ecosystem with various designs, storage possibilities, and processing capacities. The diversity of this ecosystem has several limitations, particularly in terms of security and privacy concerns. In the numerous paradigms discussed, this systematic literature review highlights overlaps, distinctions, direct assaults, and defences. The primary conclusion identifies the most critical security and privacy risks. The results highlight significant parallels and divergences between the cloud, edge, and fog computing models. The research also revealed that this ecosystem’s heterogeneity has problems and significantly hinders the adoption of security and privacy procedures from defending against security breaches and privacy leaks. The review investigations discovered many deployment strategies to improve safety and privacy flaws. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fog computing

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