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

共 34218 条记录，以下是121-130 订阅

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MARO: Enabling Full MPI Automatic Refactoring in DSL-Based Programming Framework 24th

MARO: Enabling Full MPI Automatic Refactoring in DSL-Based ...

引用

24th international conference on Algorithms and Architectures for parallel Processing, ICA3PP 2024

作者： Lei, Tong Chen, Zongjing Che, Yonggang Xu, Chuanfu Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha410073 China National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China

ISBN: (纸本)9789819615445

Currently, the landscape of computer hardware architecture presents the characteristics of heterogeneity and diversity, prompting widespread attention to cross-platform portable parallel programming techniques. Most existing portable programming approaches adopt the "MPI+X" strategy, focusing mainly on node-level "X" parallelism, while inter-node parallelization still relies on manual MPI programming. OP2 is a domain-specific language-based portable parallel programming framework for unstructured mesh applications that supports the automatic generation of MPI parallel code. However, programmers must manually write pre-processing code during code refactoring, such as handling the mapping relationship between global and process-local variables and the initial distribution of process-local data. In this paper, we propose MARO (MPI Automatic Refactoring for OP2), an automatic refactoring method for generating pre-processing code targeting MPI code generation within the OP2 programming framework for unstructured mesh applications. This method automatically generates the pre-processing code required for MPI parallelism through the source-to-source translator, enabling OP2 to generate MPI parallel code without requiring users to write MPI-related code manually. We compare the automatically refactored OP2 applications using the proposed method with those manually refactored to validate its effectiveness. The proposed method enhances OP2’s automatic parallel code generation capability, enabling it to achieve fully automatic MPI parallelization. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Automatic programming

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Partial Order-centered Hyperbolic Representation Learning for Few-shot Relation Extraction 31

Partial Order-centered Hyperbolic Representation Learning fo...

引用

31st international conference on Computational Linguistics, COLING 2025

作者： Hu, Biao Huang, Zhen Hu, Minghao Yang, Pinglv Qiao, Peng Dou, Yong Wang, Zhilin National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology China Center of Information Research Academy of Military Science China College of Meteorology and Oceanology National University of Defense Technology China

ISBN: (纸本)9798891761964

Prototype network-based methods have made substantial progress in few-shot relation extraction (FSRE) by enhancing relation prototypes with relation descriptions. However, the distribution of relations and instances in distinct representation spaces isolates the constraints of relations on instances, making relation prototypes biased. In this paper, we propose an end-to-end partial order-centered hyperbolic representation learning (PO-HRL) framework, which imposes the constraints of relations on instances by modeling partial order in hyperbolic space, so as to effectively learn the distribution of instance representations. Specifically, we develop the hyperbolic supervised contrastive learning based on Lorentzian cosine similarity to align representations of relations and instances, and model the partial order by constraining instances to reside within the Lorentzian entailment cone of their respective relation. Experiments on three benchmark datasets show that PO-HRL outperforms the strong baselines, especially in 1-shot settings lacking relation descriptions. © 2025 Association for Computational Linguistics.

关键词： Contrastive Learning

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LDChain: A Lightweight and Scalable Blockchain System for Dynamic IoT Scenarios 20th

LDChain: A Lightweight and Scalable Blockchain System for ...

引用

20th IFIP WG 10.3 international conference on Network and parallel computing, NPC 2024

作者： Wang, Jianrong Cao, Yuxuan Hu, Dengcheng Li, Qi Li, Sen Li, Xuewei Liu, Xiulong College of Intelligence and Computing Tianjin University Tianjin China School of Electrical and Information Engineering Tianjin University Tianjin China School of Future Technology Tianjin University Tianjin China

ISBN: (纸本)9789819628292

As a distributed ledger, blockchain inherently guarantees data consistency. However, when applied to the Internet of Things (IoT), existing solutions employing blockchain systems struggle to address the scalability demands posed by the dynamic mobility of heterogeneous devices. To this end, we propose Lightweight DAG Chain (LDChain), a lightweight and scalable blockchain system tailored for IoT scenarios. LDChain connects weak nodes and strong nodes through a hierarchical DAG consensus algorithm. For weak nodes, we introduce a transaction-based DAG consensus mechanism utilizing a snapshot synchronization strategy to cater to the dynamic nature of weak nodes and the scalability demands of the system. The integrated snapshot synchronization strategy, based on the second latest block transaction selection algorithm, reduces the storage and computational overhead for resource-restricted devices. For strong nodes, we devise a block-based DAG consensus mechanism and a repackaging strategy, which resolves transaction conflicts across regions by managing conflicting transactions through a priority queue. We implement a prototype of LDChain and conduct experiments in a network environment comprising 10 cloud servers, simulating several strong nodes and weak nodes. The experimental results show that our system achieves a throughput exceeding 7000 transactions per second (TPS) and a transaction confirmation latency of 1.3 s with 100 nodes. © IFIP international Federation for Information Processing 2025.

关键词： Scalability

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Optimizing Cloud computing Task Schedules Through Advanced Intelligent Optimization Methods

Optimizing Cloud Computing Task Schedules Through Advanced I...

引用

international IoT, Electronics and Mechatronics conference, IEMTRONICS 2024

作者： Ammari, Ahmed Chiheb Al Hmouz, Rami Khriji, Lazhar Zhou, MengChu Department of Electrical and Computer Engineering College of Engineering Sultan Qaboos University Muscat Oman Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States

ISBN: (纸本)9789819747832

The number of distributed Green Cloud Datacenters (DGCDs) is globally increasing. Such DGCDs deploy different types of renewable sources to generate clean energy and save money. They are located in different regions depending on the availability of renewable energy sources, costs of bandwidth and grid electricity prices. This paper focuses on applications within them, which are sensitive to delay and looks into a way to schedule different applications with respect to time constraint. The paper uses the Firefly algorithm, bat algorithm and simulated annealing-bat algorithms as optimization techniques to minimize total operational cost of such DGCDs. These algorithms have been compared through data-driven experiments carried out in this study. Of particular note is the Firefly algorithm's superior performance when compared with others. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Simulated annealing

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HPC master design: experience from Pisa

HPC master design: experience from Pisa

引用

Euromicro conference on parallel, distributed and Network-Based Processing

作者： Marco Danelutto Dept. of Computer Science Univ. of Pisa

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

HPC is a widely used term, often referred to the applications, architectures and programming models and tools targeting highly parallel machines such as those of the *** lists. Recent advances in computing hardware resources require application of HPC techniques when using much smaller machines. Indeed, proper parallel programming tools and applications are needed also to exploit parallel hardware resources in personal computers (laptops, desktops, servers). This paper outlines key challenges in designing master’s degree programs in HPC and shares lessons learned from various experiences in developing and implementing such programs in Italy and Europe.

关键词： Portable computers parallel programming High performance computing Europe parallel machines Hardware Telecommunications Stakeholders Servers Programming profession

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Modal-Centric Insights Into Multimodal Federated Learning for Smart Healthcare: A Survey 24th

Modal-Centric Insights Into Multimodal Federated Learning fo...

引用

24th international conference on Algorithms and Architectures for parallel Processing, ICA3PP 2024

作者： Wang, Di Liu, Wenjian Gao, Longxiang Qu, Y. Neil. Zhang, Hu Shi, Jihong The Faculty of Data Science City University of Macau Macau China Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Medical Integration and Practice Center Shandong University Jinan China

ISBN: (纸本)9789819615476

Federated Learning (FL) has progressed, providing a distributed mechanism where data need not be consolidated, thereby enhancing the privacy and security of sensitive healthcare data. Recent advancements in multimodal machine learning have empowered the development of accurate and robust AI systems in the medical domain, especially within centralized database systems. Multimodal federated learning (MFL) combines federated learning’s distributed nature with the flexibility to work with various data types. This capability is particularly relevant in medical contexts, where healthcare data can range from electronic health records (EHR) to radiology imaging, genomic data, and wearable device information. The key advantage of MFL in medical settings is the ability to create robust models without centralizing sensitive patient information. However, there are no existent research to comprehensively review the application status of MFL in smart healthcare systems from the modal-centric aspect. Motivated by this, we explore the systematization of knowledge about multimodal federated learning on medical data, find the challenges of this area and related solutions. With this research, we hope to provide a systematical review of this under-explored domain and provide insights for forthcoming researchers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Performance Evaluation of NLP Models for European Portuguese: Multi-GPU/Multi-node Configurations and Optimization Techniques 24th

Performance Evaluation of NLP Models for European Portugue...

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24th international conference on Algorithms and Architectures for parallel Processing, ICA3PP 2024

作者： Santos, Daniel Miquelina, Nuno Schmidt, Daniela Quaresma, Paulo Nogueira, Vítor Beires VISTA Lab ALGORITMI Research Center University of Évora Évora Portugal

ISBN: (纸本)9789819615506

Natural Language Processing (NLP) research has predominantly focused on the English language, leading to a wealth of resources and advancements tailored to English. However, there is a growing need to extend these capabilities to other languages, such as European Portuguese, to ensure the inclusivity and accessibility of NLP technologies. In this study, we explore the evaluation of NLP models in the European Portuguese language using a multi-GPU/multi-node machine. We utilized various tools such as PyTorch, Accelerate, Transformers, and DeepSpeed with ZeRO Stage 3 to handle the computational demands of large-scale model training. We provide all the key aspects of our methodology to evaluate various models on translated GLUE tasks. Additionally, we introduce AiBERTa, a base model with 110 million parameters, developed and pre-trained on a corpus tailored for European Portuguese. This research highlights the effectiveness of advanced tools and distributed computing in scaling NLP model training, providing a foundation for future enhancements in European Portuguese language processing. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： distributed computer systems

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STB-GraCapsNet: A Novel Capsule Network Structure with Swin Transformer Block 25th

STB-GraCapsNet: A Novel Capsule Network Structure with Swin...

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25th international conference on parallel and distributed computing, Applications and Technologies, PDCAT 2024

作者： Zhang, Chunying Dong, Ziao Wang, Liya Liu, Lu Ren, Jing Ma, Jiang Liu, Bin College of Science North China University of Science and Technology 21 Bohai Road Caofeidian Xincheng Hebei Tangshan063210 China Big data and Social Computing Research Center Hebei University of Science and Technology Hebei Shijiazhuang0500198 China

ISBN: (纸本)9789819642069

Capsule network is a new type of neural network encoding features into capsules and constructing the part-whole relationships, which demonstrated good performance in image classification. However, it has some issues such as weak feature extraction ability and large number of training parameters, leading to performance decline when processing images with complex background. To address these issues, this article proposes a novel capsule network structure called STB-GraCapsNet, with key improvements as follows: 1) It uses Swin Transformer Block with the shift window attention mechanism for data preprocessing to obtain richer features;2) the features are then processed through Linear Embedding and passed into the Primary Capsule Layer to generate high-dimension feature capsules, which reduces primary capsules number and improves feature extraction ability of the network. Ablation and comparative experiments based on multiple datasets show that STB-GraCapsNet performs better on complex datasets: 77.8% and 36.2% reduction in parameters number of capsule structure, and 12.07% and 5.22% improvement in model accuracy, as compared to CapsNet and GraCapsNet, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Signal encoding

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Research on Big Data distributed computing Model and Algorithm Optimization for Complex Network Analysis

Research on Big Data Distributed Computing Model and Algorit...

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Digital Analysis and Processing, Intelligent Computation (DAPIC), international conference on

作者： Aiting Sun Liaoning Vocational College of light industry Liaoning China

ISBN: (数字)9798331536169

ISBN: (纸本)9798331536176

With the advent of the era of big data, the traditional stand-alone computing model can no longer meet the needs of complex network analysis, and the distributed computing model provides a new solution. This paper first introduces the importance of complex network analysis and the challenges brought by big data, then discusses the application of distributed computing model in complex network analysis in detail, and focuses on the distributed computing algorithm based on MapReduce. Then, this paper puts forward an optimization strategy of distributed community detection algorithm based on modularity optimization. This strategy performs community detection tasks in parallel through MapReduce framework, and optimizes community division through iterative updating to maximize modularity. The experimental results show that the optimized algorithm has a significant improvement in computing time, accuracy and resource consumption. The distributed computing model based on MapReduce framework and its optimization strategy have obvious advantages in dealing with large-scale complex networks. It can not only improve the calculation efficiency and accuracy, but also reduce the resource consumption, which provides an efficient and feasible solution for complex network analysis.

关键词： Analytical models Accuracy Computational modeling Memory management Complex networks Big Data Data models distributed computing Detection algorithms Optimization

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A Novel Consensus Mechanism Based on Dynamic Sharding 20th

A Novel Consensus Mechanism Based on Dynamic Sharding

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20th IFIP WG 10.3 international conference on Network and parallel computing, NPC 2024

作者： Zhang, Jingyu Teng, Yilong Sun, Yongtao Zhu, Shi Yang, Fangliao Luo, Lailong School of Computer and Communication Engineering Changsha University of Science and Technology Changsha China Science and Technology on Information Systems Engineering Laboratory School of Systems Engineering National University of Defense Technology Changsha China Data Operations Center State Grid Hunan Electric Power Information and Communication Corporation Hunan Changsha China

ISBN: (纸本)9789819628292

Blockchain, as an emerging technology, has been widely studied by the researchers from academia and industry. Alliance chain, as an important form of blockchain, is often applied to smart grids, smart city and the Internet of Things to ensure the authenticity and reliability of data. Practical Byzantine Fault Tolerance (PBFT) algorithm is a key technology to ensure data consistency in the alliance chains, and it plays a critical role in improving system performance, security and scalability. However, with the increase of the number of nodes and transactions, the ability of PBFT to process transactions is challenged. In particular, PBFT’s linear transaction processing also limits its ability to process transactions in parallel. In view of the problems existing in PBFT, this paper studies the consensus mechanism optimization, and puts forward a consensus mechanism with better performance. In this paper, in order to solve the problem of weak parallel transaction processing capability of PBFT, we propose a novel Consensus Mechanism based on Dynamic Sharding (CMDS). CMDS firstly constructs a dynamic fragmentation model based on consistent hash algorithm to segment nodes, and then constructs a voting consensus mechanism based on reputation weight to achieve global state consistency. The experimental results show that CMDS can significantly improve the ability of parallel transaction processing and the performance of blockchain system. © IFIP international Federation for Information Processing 2025.

关键词： Blockchain

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