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检索条件"任意字段=International Conference on Massively Parallel Computing Systems"

共 23919 条记录，以下是21-30 订阅

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parallel computing Optimization of Tourism Big Data based on Crawler Programs 1

Parallel Computing Optimization of Tourism Big Data based on...

引用

1st international conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Zhou, Xiaoyu Wuhan Donghu University School of Management Hubei Wuhan China

ISBN: (纸本)9798331529246

With the rapid development of the tourism industry, traditional tourism methods are undergoing significant transformation, and online tourism is gradually becoming a new highlight in the market. However, faced with the challenges of big data, online tourism data experiences tremendous pressure in terms of storage, computation, and management, and the issue of information overload seriously affects the user experience. Therefore, there is an urgent need for innovative platforms and recommendation algorithms to optimize the screening and recommendation of tourism information. This paper proposes an optimization scheme based on the Spark cloud computing platform to improve the efficiency of tourism data processing. First, utilizing Spark's distributed computing and memory management capabilities, tourism data can be stored and processed in a distributed manner, thus accelerating the computation speed of recommendation algorithms. Secondly, a Python-written crawler program is used to obtain real tourism data, including user ratings and comments on attractions, to evaluate the effectiveness of the improved algorithm. Finally, a weighted algorithm combining the LDA topic model is proposed. This algorithm analyzes the topic distribution of comment texts and weights the comments based on user ratings, calculating the predicted score for unrated attractions for the target user, thereby achieving accurate attraction recommendations. The experimental results show that the algorithm proposed in this study demonstrates significant advantages in prediction accuracy. © 2025 IEEE.

关键词： Cloud platforms

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Scheduling Big Machine Learning Tasks on Clusters of Heterogeneous Edge Devices 17

Scheduling Big Machine Learning Tasks on Clusters of Heterog...

引用

17th international conference on COMmunication systems and NETworkS, COMSNETS 2025

作者： Mathkar, Saumya Aiyer, Shreya Bapat, Yashovardhan Pinki, Pinki Paul, Arnab K. Naik, Vinayak BITS Pilani Department of CSIS Goa India

ISBN: (纸本)9798331531195

Edge computing has transformed machine learning by using computing closer to the data sources, thereby reducing latency. The ever-increasing volume of data has necessitated forming clusters of edge devices, possibly with heterogeneous capabilities. Managing heterogeneous resources such as computation and memory remains challenging. Given the capabilities of the edge devices, we need a simple technique suitable for an Edge computing *** introduce a scheduling mechanism that leverages Integer Linear Programming (ILP) to optimize the overall computation time of ML-based tasks. We implement our scheduling mechanism that efficiently allocates resources, ensuring tasks are executed parallel across the cores of edge devices in a cluster by minimizing computation time. For tasks large enough to fit on any core, we leverage distributed learning to train the model in pieces and later combine them. We employ our ILP-based scheduler for efficient task allocation and compare its performance with a Greedy, simple approach based on best-fit technique. We evaluate our approach on three cases of sets of tasks on the spectrum of uniformity and size. Our results demonstrate two times the speed gain for our ILP-based approach over Greedy for the category with the least uniformity and large size. © 2025 IEEE.

关键词： Distributed computing Edge computing Optimization

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Unleashing Multicore Strength for Efficient Execution of Blockchain Transactions 21st

Unleashing Multicore Strength for Efficient Execution of Blo...

引用

21st international conference on Distributed computing and Intelligent Technology

作者： Ravish, Ankit Tejwani, Akshay Manaswini, Piduguralla Peri, Sathya Indian Inst Technol Hyderabad Hyderabad Telangana India

ISBN: (纸本)9783031814037;9783031814044

Blockchain technology is characterized by its distributed, decentralized, and immutable ledger system which serves as a fundamental platform for managing smart contract transactions (SCTs). However, these SCTs undergo sequential validation within a block which introduces performance bottlenecks in blockchain. In response, this paper introduces a framework called the Multi-Bin parallel Scheduler (MBPS) designed for parallelizing blockchain smart contract transactions to leverage the capabilities of multicore systems. Our proposed framework facilitates concurrent execution of SCTs, enhancing performance by allowing non-conflicting transactions to be processed simultaneously while preserving deterministic order. The framework comprises of three vital stages: conflict detection, bin creation, and execution. We conducted an evaluation of our MBPS framework in Hyperledger Sawtooth v1.2.6, revealing substantial performance enhancements compared to existing parallel SCT execution frameworks across various smart contract applications. This research contributes to the ongoing optimization efforts in blockchain technology demonstrating its potential for scalability and efficiency in real-world scenarios.

关键词： Blockchain Smart Contracts parallel Execution Conflict Detection

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PPQSort: Pattern parallel Quicksort 15th

PPQSort: Pattern Parallel Quicksort

引用

15th international conference on parallel Processing and Applied Mathematics, PPAM 2024

作者： Hévr, Gabriel Šimeček, Ivan Department of Computer Systems Faculty of Information Technology Czech Technical University in Prague Thákurova 9 Prague16000 Czech Republic

ISBN: (纸本)9783031856969

This paper presents PPQSort (Pattern parallel Quicksort), a new parallel quicksort algorithm that provides high performance and ease of use. PPQSort uses C++ threads for parallelization, achieving efficient sorting without external libraries and allowing seamless integration across different computing environments. This paper describes novel quicksort optimizations, including branchless partitioning and their efficient parallel implementation. PPQSort is compared with existing parallel quicksort algorithms on different machines and with different input data. Experimental evaluation results demonstrate that PPQSort is fast and robust, consistently outperforming the fastest available parallel quicksort implementations for almost all inputs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： C++ (programming language)

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Semi-Automated Construction of Sense-Annotated Datasets for Practically Any Language 31

Semi-Automated Construction of Sense-Annotated Datasets for ...

引用

31st international conference on Computational Linguistics, COLING 2025

作者： Riley, Jai Hauer, Bradley Hriti, Nafisa Sadaf Luo, Guoqing Mirzaei, Amirreza Sheikhi, Ali Rafiei Hadi Siavashpour, Mahvash Tavakoli, Mohammad Shi, Ning Kondrak, Grzegorz Alberta Machine Intelligence Institute Department of Computing Science University of Alberta Edmonton Canada

ISBN: (纸本)9798891761964

High-quality sense-annotated datasets are vital for evaluating and comparing WSD systems. We present a novel approach to creating parallel sense-annotated datasets, which can be applied to any language that English can be translated into. The method incorporates machine translation, word alignment, sense projection, and sense filtering to produce silver annotations, which can then be revised manually to obtain gold datasets. By applying our method to Farsi, Chinese, and Bengali, we produce new parallel benchmark datasets, which are vetted by native speakers of each language. Our automatically-generated silver datasets are of higher quality than the annotations obtained with recent multilingual WSD systems, particularly on non-European languages. © 2025 Association for Computational Linguistics.

关键词： WSDL

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Energy-Aware Node Selection for Cloud-Based parallel Workloads with Machine Learning and Infrastructure as Code 15

Energy-Aware Node Selection for Cloud-Based Parallel Workloa...

引用

15th international conference on Cloud computing and Services Science, CLOSER 2025

作者： Citadin, Denis B. Rossi, Fábio Diniz Luizelli, Marcelo C. Navaux, Philippe O.A. Lorenzon, Arthur F. Institute of Informatics Federal University of Rio Grande do Sul Brazil Federal Institute Farroupilha Campus Alegrete Brazil Federal University of Pampa Campus Alegrete Brazil

ISBN: (纸本)9789897587474

Cloud computing has become essential for executing high-performance computing (HPC) workloads due to its on-demand resource provisioning and customization advantages. However, energy efficiency challenges persist, as performance gains from thread-level parallelism (TLP) often come with increased energy consumption. To address the challenging task of optimizing the balance between performance and energy consumption, we propose SmartNodeTuner. It is a framework that leverages artificial intelligence and Infrastructure as Code (Iac) to optimize performance-energy trade-offs in cloud environments and provide seamless infrastructure management. SmartNodeTuner is split into two main modules: a BuiltModel Engine leveraging an artificial neural network (ANN) model trained to predict optimal TLP and node configurations;and AutoDeploy Engine using IaC with Terraform to automate the deployment and resource allocation, reducing manual efforts and ensuring efficient infrastructure management. Using ten well-known parallel workloads, we validate SmartNodeTuner on a private cloud cluster with diverse architectures. It achieves a 38.2% improvement in the Energy-Delay Product (EDP) compared to Kubernetes’ default scheduler and consistently predicts near-optimal configurations. Our results also demonstrate significant energy savings with negligible performance degradation, highlighting SmartNodeTuner ’s effectiveness in optimizing resource use in heterogeneous cloud environments. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Energy efficiency

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Research on new energy grid-connected human-machine cooperative training system applying parallel control and transformer modeling 3

Research on new energy grid-connected human-machine cooperat...

引用

3rd international conference on Algorithms, Network, and Communication Technology, ICANCT 2024

作者： Li, Miao Wang, Jing Zhuang, Yuan Training Center of Jilin Province State Grid Corporation of China Changchun China

ISBN: (数字)9781510688902

ISBN: (纸本)9781510688896

The purpose of this paper is to propose a panoramic human-machine collaborative training system that can adapt to new energy grid-connected operation conditions, simulate various complex situations, and provide regulators with simulation exercises, intelligent deductions, decision-making references, Q&A services, which provide intelligent reference solutions for precise regulation under new energy access scenarios with high percentage of new energy under a new type of power system. In order to enhance training effectiveness and operational efficiency, it is need to apply intelligent technology and data-driven models instead of experience and human labor. Interactive Q&A application such as Chat GPT has subversively changed the implementation mode of the training process, making the traditional teaching of theory and basic knowledge easier and faster. However, these commercial Q&A systems still have limitations in terms of accuracy, security, as well as professionalism, which makes them inefficient in professional learning area. In this paper, we use the idea of parallel control and the transformer model to construct a human-computer cooperative training system adapted to new energy grid-connected electric power systems, which realize a human-computer cooperative training model that highly integrates the Q&A services with the real trainer, the real trainees, the computer simulation system. By constructing a large model of human-computer system, a training computing experiment platform, as well as a system with a closed loop of training reality, the parallel training will help training program planning, training teaching design, training arrangements, teaching interaction and other key training links, and realize automated and intelligent training design and execution. As a training and management model adapted to the situation of artificial intelligence, parallel training will bring brand new possibilities for the development of the training industry in the intellige

关键词： Decision making

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Enhanced Indoor Pathfinding Navigation in Multistoried Buildings Using a Hybrid of RRT-Connect and Dijkstra's Algorithm

Enhanced Indoor Pathfinding Navigation in Multistoried Build...

引用

2025 international conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Ramamoorthy, S. Yadav, Abhishek Pal, Subha Bal School of Computing Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Tamil Nadu Chennai603203 India

ISBN: (纸本)9798331509828

This paper presents a hybrid approach to enhance indoor pathfinding and navigation within complex multistory environments by integrating RRT-Connect and Dijkstra's algorithm. The objective is to address the limitations of traditional algorithms like Dijkstra and A*, which struggle with computational inefficiency and adaptability in dynamic, high-dimensional spaces. The proposed solution leverages the rapid path generation capabilities of RRT-Connect, combined with Dijkstra's algorithm for refining and optimizing the final route to ensure accuracy and optimality. To further improve efficiency, parallel processing techniques utilizing multi-threaded CPU operations are incorporated, enabling real-time computation even in dynamic environments. Additionally, a frontend visualization system is developed using React, which provides users with real-time rendering of path nodes on multistory floor plan images, enhancing usability and system interactivity. Experimental findings reveal that the hybrid approach achieves a significant reduction in computation time - up to 25% compared to traditional methods - while maintaining high pathfinding accuracy. This novel combination of rapid exploration, optimal path refinement, and real-time visualization offers a robust and scalable solution for navigating complex multistory environments, making it a significant advancement in the field of indoor navigation. © 2025 IEEE.

关键词： Visualization

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Scalable High-Performance Community Detection Using Label Propagation in Massive Networks 16th

Scalable High-Performance Community Detection Using Label Pr...

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16th international conference on Social Networks Analysis and Mining

作者： Boddu, Sharon Khan, Maleq Texas A&M Univ Dept Elect Engn & Comp Sci Kingsville TX 78363 USA

ISBN: (纸本)9783031785405;9783031785412

Community detection is the problem of finding naturally forming clusters in networks. It is an important problem in mining and analyzing social and other complex networks. Community detection can be used to analyze complex systems in the real world and has applications in many areas, including network science, data mining, and computational biology. Label propagation is a community detection method that is simpler and faster than other methods such as Louvain, InfoMap, and spectral-based approaches. Some real-world networks can be very large and have billions of nodes and edges. Sequential algorithms might not be suitable for dealing with such large networks. This paper presents distributed-memory and hybrid parallel community detection algorithms based on the label propagation method. We incorporated novel optimizations and communication schemes, leading to very efficient and scalable algorithms. We also discuss various load-balancing schemes and present their comparative performances. These algorithms have been implemented and evaluated using large high-performance computing systems. Our hybrid algorithm is scalable to thousands of processors and has the capability to process massive networks. This algorithm was able to detect communities in the Metaclust50 network, a massive network with 282 million nodes and 42 billion edges, in 654 s using 4096 processors.

关键词： Community detection parallel graph algorithms network analysis graph mining

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PipeLLM: Fast and Confidential Large Language Model Services with Speculative Pipelined Encryption 25

PipeLLM: Fast and Confidential Large Language Model Services...

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30th ACM international conference on Architectural Support for Programming Languages and Operating systems, ASPLOS 2025

作者： Tan, Yifan Tan, Cheng Mi, Zeyu Chen, Haibo Institute of Parallel and Distributed Systems Seiee Shanghai Jiao Tong University Shanghai China Northeastern University Boston United States

ISBN: (纸本)9798400706981

Confidential computing on GPUs, like NVIDIA H100, mitigates the security risks of outsourced Large Language Models (LLMs) by implementing strong isolation and data encryption. Nonetheless, this encryption incurs a significant performance overhead, reaching up to 52.8% and 88.2% throughput drop when serving OPT-30B and OPT-66B, respectively. To address this challenge, we introduce PipeLLM, a user-transparent runtime system. PipeLLM removes the overhead by overlapping the encryption and GPU computation through pipelining-an idea inspired by the CPU instruction pipelining-thereby effectively concealing the latency increase caused by encryption. The primary technical challenge is that, unlike CPUs, the encryption module lacks prior knowledge of the specific data needing encryption until it is requested by the GPUs. To this end, we propose speculative pipelined encryption to predict the data requiring encryption by analyzing the serving patterns of LLMs. Further, we have developed an efficient, low-cost pipeline relinquishing approach for instances of incorrect predictions. Our experiments show that compared with vanilla systems without confidential computing (e.g., vLLM, PEFT, and FlexGen), PipeLLM incurs modest overhead ( © 2025 ACM.

关键词： Modeling languages

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