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Large-scale Integration of Remotely Sensed and GIS road networks: a full image-vector conflation approach based on optimization and deep learning

Large-scale Integration of Remotely Sensed and GIS road netw...

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ACIS international conference on Software Engineering, Artificial Intelligence, Networking, and parallel/distributed computing (SNPD)

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Efficient VLSI Architectures of Multimode 2D FIR Filter Bank using distributed Arithmetic Methodology 6th

Efficient VLSI Architectures of Multimode 2D FIR Filter Bank...

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6th international conference on Soft computing and Signal Processing, ICSCSP 2023

作者： Odugu, Venkata Krishna Satish, B. Janardhana Rao, B. Gade, Harish Babu Department of ECE CVR College of Engineering Hyderabad India

ISBN: (纸本)9789819984503

For the implementation of the hardware structure of filter architecture, the area, power, and delay efficiency are needed. Memory complexity is also important in the 2D FIR filter architecture, while used for image processing applications. In this work, a memory-efficient 2D FIR filter bank architecture is designed using parallel processing, symmetry, and distributed arithmetic methodology. The symmetry concept decreases the quantity of the multipliers of the filter architecture. parallel processing and multimode filter bank approaches will improve memory efficiency through memory reuse and memory sharing, respectively. The distributed arithmetic (DA)-based filter architectures reduce the multiplier complexity in terms of power and area. Four types of symmetry filter architectures along with one normal filter (without symmetry) are integrated as a multimode filter bank. The required filter can be selected by the control logic. In this filter bank, the memory module is shared by all the sub-filter, which is called memory sharing. The LUT-based DA methodology is used to implement the multipliers of each sub-filter arithmetic module. The proposed multimode filter bank architecture is implemented for the filter length N = 4 with two-level parallel processing using Xilinx Vivado tools. The utilization of resources is correlated with works mentioned in the literature. The area, timing delay and power consumption reports are generated using Genus synthesis tools in 45 nm CMOS technology from the Cadence vendor. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Filter banks

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An Efficient distributed Graph Engine for Deep Learning on Graphs

An Efficient Distributed Graph Engine for Deep Learning on G...

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2023 international conference on High Performance computing, Network, Storage, and Analysis, SC Workshops 2023

作者： Deng, Gangda Akgül, Ömer Faruk Zhou, Hongkuan Zeng, Hanqing Xia, Yinglong Li, Jianbo Prasanna, Viktor University of Southern California Los AngelesCA United States Meta Menlo ParkCA United States

ISBN: (纸本)9798400707858

Traditional graph-processing algorithms have been widely used in Graph Neural networks (GNNs). This combination has shown state-of-the-art performance in many real-world network mining tasks. Current approaches to graph processing in deep learning face two main problems. On the one hand, easy-to-use deep learning libraries lack support for widely used graph-processing algorithms and do not provide low-level APIs for building distributed graph-processing algorithms. On the other hand, existing graph-processing libraries are not user-friendly for deep learning researchers. Their graph primitives are not designed for batch processing, which is essential for deep learning use cases. In this paper, we present an efficient and easy-to-use graph engine that incorporates distributed graph processing into deep learning ecosystems. We develop a distributed graph storage system with an efficient batching technique to minimize communication overhead incurred by Remote Procedure Calls (RPC) between computing nodes. We propose an optimized method for distributed computation of Single Source Personalized PageRank (SSPPR) using the Forward Push algorithm based on lock-free parallel maps. Experimental evaluations demonstrate significant improvement, with up to three orders of magnitude in SSPPR throughput, of our graph engine compared with the tensor-based implementation. Both methods offer necessary usability with tensor operations, which are widely used for graph processing in current deep graph libraries. © 2023 Owner/Author.

关键词： Engines

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18th international conference on Algorithmic Aspects in Information and Management, AAIM 2024

18th International Conference on Algorithmic Aspects in Info...

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18th international conference on Algorithmic Aspects in Information and Management, AAIM 2024

ISBN: (纸本)9789819778003

The proceedings contain 45 papers. The special focus in this conference is on Algorithmic Aspects in Information and Management. The topics include: Approximation Algorithm for the Maximum Interval Multi-cover Problem;minsum Problem for Discrete and Weighted Set Flow on Dynamic Path Network;efficient Approximation Algorithms for parallel Batch Machine Scheduling of Malleable Jobs;cost-Sharing Mechanisms for the Selfish Open-End Bin Packing Problem;ToI-Based Data Utility Maximization for UAV-Assisted Wireless Sensor networks;parity-Constrained Weighted k-Center;optimal Bridge, Twin Bridges and Beyond: Inserting Edges into a Road Network to Minimize the Constrained Diameters;Dynamic DR-Submodular Maximization with Linear Costs over the Integer Lattice;a distributed Approximation Algorithm for the Total Dominating Set Problem;revisit the Online Facility Location Problem with Uniform Facility Cost;or-Submodular Maximization Under a Matroid Constraint and a Knapsack Constraint;a distributed Method for Negative Content Spread Minimization on Social networks;approximation Algorithms for the Minimum Weight Cycle/Path Partition Problem;iterative Rounding for Bag of Tasks Scheduling with Rejection in High Performance computing;integrated Airline Aircraft Routing and Crew Pairing by Alternating Lagrangian Decomposition;monotone Submodular Meta-learning under the Matroid Constraint;submodular Participatory Budgeting;approximation Algorithms for k-Submodular Maximization Under the Fair Constraints and Size Constraints;systematic Review of Cybersecurity Disclosure Research;revisiting the Stretch Factor of Delaunay Triangulations of Points in Convex Position;transition-Based Heterogeneous Network Embedding by Self-attention Model of Sparse Matrix.

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Mbapp: Efficient Memory-Balanced Pipeline parallelism for Large Model Fine-Tuning on Commodity GPU Servers 24

Mbapp: Efficient Memory-Balanced Pipeline Parallelism for La...

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5th international conference on Computer Information and Big Data Applications, CIBDA 2024

作者： Liu, Yujie Lai, Zhiquan Li, Dongsheng National Key Laboratory of Parallel and Distributed Computing College of Computer National University of Defense Technology Changsha410000 China

ISBN: (纸本)9798400718106

Large-scale models have demonstrated outstanding performance across various downstream tasks. Pipeline parallelism is essential for fine-tuning large models on commodity GPU servers, as it plays a crucial role in making their exceptional performance more accessible and widespread. Existing approaches have encountered challenges in attaining effective memory-balanced pipeline parallelism. This paper presents a novel memory load-balanced pipeline parallel solution that aims to distribute memory usage evenly across stages on commodity GPU servers by leveraging NVLink bridges. The solution presents a novel approach for transferring data from GPUs to CPUs through the PCI-e link between adjacent GPUs interconnected by the NVLink bridge. Moreover, our methodology orchestrates data transfer operations to reduce offloading latency during the fine-tuning of large models. The evaluation demonstrates that our approach enhances switching efficiency by 1.46x to 1.76x and improves throughput by 2.04x to 3.59x compared to the PyTorch offloading technique. © 2024 ACM.

关键词： Deep neural networks

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18th international conference on Algorithmic Aspects in Information and Management, AAIM 2024

18th International Conference on Algorithmic Aspects in Info...

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18th international conference on Algorithmic Aspects in Information and Management, AAIM 2024

ISBN: (纸本)9789819777976

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distributed Task Offloading for IoAV Using DDP-DQN 1

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23rd international conference on Algorithms and Architectures for parallel Processing (ICA3PP)

作者： Peng, Xiting Li, Wenjie Zhang, Xiaoyu Dong, Mianxiong Ota, Kaoru Song, Shun Shenyang Univ Technol Sch Informat Sci & Engn Shenyang 110870 Peoples R China Shenyang Univ Technol Sch Artificial Intelligence Shenyang 110870 Peoples R China Shenyang Key Lab Informat Percept & Edge Comp Shenyang Peoples R China Muroran Inst Technol Dept Informat & Elect Engn Muroran Hokkaido 0508585 Japan

ISBN: (数字)9789819708116

ISBN: (纸本)9789819708109;9789819708116

The swift progress of the Internet of Vehicles (IoV) and autonomous driving technology has facilitated the emergence of the Internet of Autonomous Vehicles (IoAV). If delay-sensitive vehicle tasks are not completed on time, it will lead to bad consequences for IoAV. Task offloading technology can solve the problem that the vehicle cannot meet the task requirements. However, highly dynamic vehicle networks and diverse vehicle applications require more intelligent task offloading strategies. Therefore, this paper addresses the distributed task offloading problem in the IoAV to meet diverse vehicle task demands. First, we model the vehicle task offloading problem as a decision problem, and a deep reinforcement learning (DRL) algorithm named DDP-DQN (double-dueling-prioritize-DQN) is applied to complete vehicle tasks more efficiently. Then, we design a reward function to complete the task within the acceptable maximum delay of the task while reducing the consumption of resources. Simulations demonstrate the outperforming of the DDP-DQN compared with other three reinforcement learning algorithms.

关键词： Internet of autonomous vehicles Task offloading Vehicle edge computing Deep reinforcement learning

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An Auxiliary Decision Model for Distribution Network Dispatching Based on Scientific computing 20

An Auxiliary Decision Model for Distribution Network Dispatc...

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20th international conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2024

作者： Li, Qiang Ouyang, Hong Zheng, Jianning Xiang, Hui Zhao, Feng Zhao, Linlin Wang, Guaiqiang Li, Wenpu State Grid Information & Telecommunication Group CO. LTD. Beijing China Beijing Sgitg Accenture Information Technology Center CO. LTD. Beijing China

ISBN: (纸本)9798350356328

As distributed generation resources are interconnected on a large scale, the management and dispatching of distribution networks have become increasingly complex, and there is an urgent need for the auxiliary decision-making of distribution network dispatching to transition towards intelligence. Currently, operations such as load transfer still rely on manual handling, encountering issues like low digitalization levels and poor plan optimization. Decisions on load transfer strategies fail to utilize human-machine intelligent collaboration for adaptive decision-making in actual operational contexts. Therefore, an auxiliary decision model for distribution network dispatching based on scientific computing is proposed. Firstly, an optimized model for load transfer in distribution networks based on the collaborative optimization of primary and secondary distribution was developed. The objective is to minimize the operation costs of the transmission network and the costs of load shedding in the distribution network, thereby refining load supply transfer strategies and improving the computational efficiency and the optimality of these strategies. Secondly, an intent understanding and information recognition model is built upon LLaMA -CRF and knowledge graph methodologies. This model utilizes the LLaMA large model in conjunction with a parallel application architecture of the knowledge graph to facilitate the comprehension of intentions and the extraction of load supply transfer information. Finally, by establishing evaluation indicators and a test dataset for the load transfer operation auxiliary decision-making model, the effectiveness of intent understanding, information extraction, and optimization strategies for load transfer are assessed, verifying the validity of the proposed scientific computing-based auxiliary decision-making model for distribution network dispatching and operation. © 2024 IEEE.

关键词： Knowledge graph

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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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Aware: Adaptive distributed Training with Computation, Communication and Position Awareness for Deep Learning Model 24

Aware: Adaptive Distributed Training with Computation, Commu...

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24th IEEE international conference on High Performance computing and Communications, 8th IEEE international conference on Data Science and Systems, 20th IEEE international conference on Smart City and 8th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Zeng, Yan Yi, Guangzheng Yin, Yuyu Wu, Jiyang Xue, Meiting Zhang, Jilin Wan, Jian Zhang, Yunquan School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China School of Cyberspace Hangzhou Dianzi University Hangzhou China Institute of Computing Technology Chinese Academy of Sciences State Key Laboratory of Computer Architecture Beijing China

ISBN: (纸本)9798350319934

The accuracy of the neural networks can usually be improved by increasing the size of the dataset and the layers or operators of the network, as it has strong composability. But, it makes a challenge to train these models efficiently, due to the limited resources of a single device, such as memory, computational resource and so on. For this challenge, it has become an inevitable trend to automatically implement model parallelism across multiple devices. This paper proposes Aware, an adaptive distributed parallel training method, to search distributed parallel strategy automatically for deep learning model. It firstly proposes an operator fusion strategy with computation and communication awareness to simplify the computational graph of deep learning model. And then, it introduces position-aware graph embedding algorithm to extract the structural features of models, which can make the searched parallel strategies to transplant to other networks with similar structures. On this basis, we use reinforcement learning algorithm to search distributed parallel strategy automatically. This paper makes experiments with neural networks such as Inception V3, NMT, GNM, NasNet and ResNet, and uses CIFAR10 and PTB datasets to compare the two methods of Hierarchical and Placeto. The results show that compared with Placeto, Aware achieves up to 5% reductions in runtime. Aware achieves more than 8% reductions in runtime compared with Hierarchical. Moreover, it has better transplantation and generalization capabilities, and supports pre-training for large-scale network parallel strategy search and accelerates convergence. © 2022 IEEE.

关键词： Deep learning

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