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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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parallel Algorithms for Equilevel Predicates 24

Parallel Algorithms for Equilevel Predicates

引用

25th international conference on distributed computing and Networking, ICDCN 2024

作者： Garg, Vijay K Streit, Robert P The University of Texas at Austin AustinTX United States

ISBN: (纸本)9798400716737

We define a new class of predicates called equilevel predicates on a distributive lattice which eases the analysis of parallel algorithms. Many combinatorial problems such as the vertex cover problem, the bipartite matching problem, and the minimum spanning tree problem can be modeled as detecting an equilevel predicate. The problem of detecting an equilevel problem is NP-complete, but equilevel predicates with the helpful property can be detected in polynomial time in an online manner. An equilevel predicate has the helpful property with a polynomial time algorithm if the algorithm can return a nonempty set of indices such that advancing on any of them can be used to detect the predicate. Furthermore, the refined independently helpful property allows online parallel detection of such predicates in NC. When the independently helpful property holds, advancing on all the specified indices in parallel can be used to detect the predicate in polylogarithmic time. We also define a special class of equilevel predicates called solitary predicates. Unless NP= RP, this class of predicate also does not admit efficient algorithms. Earlier work has shown that solitary predicates with the efficient advancement can be detected in polynomial time. We introduce two properties called the antimonotone advancement and the efficient rejection which yield the detection of solitary predicates in NC. Finally, we identify the minimum spanning tree, the shortest path, and the conjunctive predicate detection as problems satisfying such properties, giving alternative certifications of their NC memberships as a result. © 2024 Owner/Author.

关键词： parallel algorithms

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DPSPC: A Density Peak-Based Statistical parallel Clustering Algorithm for Big Data 17th

DPSPC: A Density Peak-Based Statistical Parallel Clustering ...

引用

17th international conference on Knowledge Science, Engineering and Management (KSEM)

作者： Pan, Xiaohui Deng, Jinglan Yang, Hanyu Peng, Jing Yin, Jianfei Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China

ISBN: (纸本)9789819754946;9789819754953

Data parallelism is a powerful design paradigm for clustering tasks involving large datasets. However, existing solutions suffer from three problems: (i) using non-identical distribution based partitioning methods may pose the risk of data skew;(ii) frequent communication among the divided data partitions may lead to potential performance degradation;and (iii) unnecessary full computation results in significant computational overhead. In order to address these issues, we propose a density peak-based statistical parallel clustering algorithm for big data (DPSPC). Our sampling-based approach creates equal-sized data blocks with the same statistical measures of clusters, reducing data skew and eliminating inter-block communication. By sampling a subset of blocks for computation, we avoid full computation. The experimental results suggest that the NMI index of the DPSPC algorithm is generally not 10% lower than that of other distributed density peak clustering algorithms, with runtime about one-tenth and the lowest communication volume.

关键词： Statistical parallel distributed Clustering Density Peak Random Sample Partition Approximate Computation

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New Algorithms for distributed Fair k-Center Clustering: Almost Accurate as Sequential Algorithms 23

New Algorithms for Distributed Fair k-Center Clustering: Alm...

引用

23rd international conference on Autonomous Agents and Multiagent Systems, AAMAS 2024

作者： Wu, Xiaoliang Feng, Qilong Huang, Ziyun Xu, Jinhui Wang, Jianxin School of Computer Science and Engineering Central South University Xiangjiang Laboratory Changsha China Department of Computer Science and Software Engineering Penn State Erie The Behrend College Erie United States Department of Computer Science and Engineering State University of New York at Buffalo Buffalo United States Hunan Provincial Key Lab on Bioinformatics Central South University Xiangjiang Laboratory Changsha China

Fair clustering problems have been paid lots of attention recently. In this paper, we study the k-Center problem under the group fairness and data summarization fairness constraints, denoted as Group Fair k-Center (GFkC) and Data Summarization Fair k-Center (DSFkC), respectively, in the massively parallel computational (MPC) distributed model. The previous best results for the above two problems in the MPC model are a 9-approximation with violation 7 (WWW 2022) and a (17 + ∊)-approximation without fairness violation (ICML 2020), respectively. In this paper, we obtain a (3 + ∊)- approximation with violation 1 for the GFkC problem in the MPC model, which is almost as accurate as the best known approximation ratio 3 with violation 1 for the sequential algorithm of the GFkC problem. Moreover, for the DSFkC problem in the MPC model, we obtain a (4 + ∊)-approximation without fairness violation, which is very close to the best known approximation ratio 3 for the sequential algorithm of the DSFkC problem. Empirical experiments show that our distributed algorithms perform better than existing state-of-the-art distributed methods for the above two problems. © 2024 international Foundation for Autonomous Agents and Multiagent Systems.

关键词： Machine learning

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Evaluation of architecture-aware optimization techniques for Convolutional Neural Networks 31

Evaluation of architecture-aware optimization techniques for...

引用

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

作者： Marichal, Raul Toyos, Guillermo Dufrechou, Ernesto Ezzatti, Pablo Instituto de Computación Universidad de la República Facultad de Ingeniería Montevideo Uruguay

ISBN: (纸本)9798350337631

The growing need to perform Neural network inference with low latency is giving place to a broad spectrum of heterogeneous devices with deep learning capabilities. Therefore, obtaining the best performance from each device and choosing the most suitable platform for a given problem has become challenging. This paper evaluates multiple inference platforms using architecture-aware optimizations for convolutional neural networks. Specifically, we use TensorRT and OpenVINO frameworks for hardware optimizations on top of the platform-aware NetAdapt algorithm. The experimental evaluation shows that on MobileNet and AlexNet, using NetAdapt with TensorRT or Open-VINO can improve latency up to 10 x and 5.3 x, respectively. Moreover, a throughput test using different batch sizes showed variable performance improvement on the different devices. Discussing the experimental results can guide the selection of devices and optimizations for different AI solutions. © 2023 IEEE.

关键词： Edge computing

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distributed Red Fox Optimized CatBoost Algorithm for Medical Anomaly Detection 5

Distributed Red Fox Optimized CatBoost Algorithm for Medical...

引用

5th international conference on Electronic Engineering and Informatics, ICEEI 2023

作者： Chen, Hongwei Wu, Tao Chen, Lun School of Computer Science Hubei University of Technology Hubei Wuhan China

ISBN: (纸本)9781643685021

Abnormality detection is a very popular research direction in the medical field;however, most of the existing methods have high requirements on the data type of the dataset and require tedious feature engineering. To address the above problems, this paper proposes a distributed optimized category-based feature boosting algorithm for constructing efficient anomaly detection models. The algorithm uses CatBoost for the numericalization of category-type features and model training and introduces the improved Red Fox optimization algorithm for parameter optimization to enhance the accuracy of the model. Finally, the Spark distributed computing framework is utilized for distributed training of the model to reduce machine configuration requirements and improve efficiency. Experiments are conducted using multiple medical datasets, and the experimental results show that the optimized CatBoost model performs well on multiple metrics, and the distributed training reduces the training time by approximately 70% compared to the stand-alone training time, which proves the effectiveness of the method. © 2024 The Authors.

关键词： Anomaly detection

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Lock-Free Bucketized Cuckoo Hashing 29th

Lock-Free Bucketized Cuckoo Hashing

引用

29th international conference on parallel and distributed computing (Euro-Par)

作者： Li, Wenhai Cheng, Zhiling Chen, Yuan Li, Ao Deng, Lingfeng Wuhan Univ Wuhan Peoples R China

ISBN: (纸本)9783031396977;9783031396984

Concurrent hash tables are one of the fundamental building blocks for cloud computing. In this paper, we introduce lock-free modifications to in-memory bucketized cuckoo hashing. We present a novel concurrent strategy in designing a lock-free hash table, called LFBCH, that paves the way towards scalability and high space efficiency. To the best of our knowledge, this is the first attempt to incorporate lock-free technology into in-memory bucketized cuckoo hashing, while still providing worst-case constant-scale lookup time and extremely high load factor. All of the operations over LFBCH, such as get, put, "kick out" and rehash, are guaranteed to be lock-free, without introducing notorious problems like false miss and duplicated key. The experimental results indicate that under mixed workloads with up to 64 threads, the throughput of LFBCH is 14%-360% higher than other popular concurrent hash tables.

关键词： buckized cuckoo hashing lock-free data structure multicore parallel computing

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Multi-Terminal Collaborative distributed Garden Planning System Integrating CG Technology 3

Multi-Terminal Collaborative Distributed Garden Planning Sys...

引用

3rd international conference on Pervasive computing and Social Networking, ICPCSN 2023

作者： Wang, Ye Liu, Qi School of Art Design and Media Sanda University Shanghai201209 China

ISBN: (纸本)9798350322842

The fast distributed garden planning system in colleges and universities is one of the mainstream applications of contemporary computers. The latest traditional methods usually use the GPU full virtualization technology as an entry point to improve the problem of a single user and a single computer, so that a physical computer can shared by multiple users without interfering with each other. With this basis, this research study designs the novel multi-terminal collaborative distributed garden planning system integrating CG technology. Key innovations are: (1) The parallel SSOR-AI preconditioning combined with the CG algorithm is proposed to improve the traditional CG model. (2) The designed multi-terminal collaboration model considers the heterogeneous structure that will be more robust for the complex data. (3) The virtual image model is designed to finish the whole work flow. In the experiment part, the data is collected from Baidu search engine with the size of more than 1000000, the various tests are conducted to validate the performance. © 2023 IEEE.

关键词： Search engines

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Leveraging Blockchain Technology for Effective Healthcare Solutions: Requirements, Architecture, Applications and Recent advances 4

Leveraging Blockchain Technology for Effective Healthcare So...

引用

4th IEEE international conference on computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Kumar, Nitin Bhushan, Bharat Sharda University Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350306118

Blockchain technology is a very high powered technology that encounters a vast variety of features. For cloud-edge systems to do data integrity audits blockchain is an authentic tool. Using blockchain hospitals can become nodes to put up distributed ledger that make sure the combat collusion and tamper resistance. It emphasize the role of blockchain in ensuring data integrity, managing access, data security and privacy based on the context of healthcare. Patient histories, healthcare management, efficient data records, access to healthcare datasets that are facilitates by blockchain in the healthcare sector. This paper examines the relation between healthcare sector and blockchain technology that shows features and recent advances. This paper concludes the major role of blockchain in the healthcare sector in developing a well-organized and secured health records. © 2023 IEEE.

关键词： Blockchain

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GANDPS: Ensuring Both Detection Precision and Stability in GAN-based Anomaly Detection for Network Traffic 21

GANDPS: Ensuring Both Detection Precision and Stability in G...

引用

21st IEEE international Symposium on parallel and distributed Processing with Applications, 13th IEEE international conference on Big Data and Cloud computing, 16th IEEE international conference on Social computing and Networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Li, Liuhuan Yang, Ximin Zhang, Xiao Yang, Yumeng Chen, Xuefeng Tang, Wan South-Central Minzu University School of Computer Science Wuhan China Key Laboratory of Cyber-Physical Fusion Intelligent Computing State Ethnic Affairs Commission China China Telecom Group GuiZhou Co. Ltd. Guiyang China

ISBN: (纸本)9798350329223

With the ever-growing network traffic and the vast amount of abnormal traffic being created, anomaly detection methods have attracted close attention in the cybersecurity domain. Generative adversarial networks (GANs) have been applied to network traffic anomaly detection, but obtaining high-precision and consistently stable detection results is still challenging for current methods. In the paper, we propose an improved GAN-based anomaly detection method called GANDPS for network traffic. The improved GAN introduces an encoder based on the original GAN, and a specialized layer is integrated into the encoder to process abnormal data. This improvement enables the encoder to effectively capture the features of normal data while distinguishing the features of abnormal data. In addition, an analogous reconstruction training strategy is employed between the generator and the encoder to balance the adversarial training between the generator and the discriminator, thereby improving the detection precision and stability of the GAN-based model. This comprehensive enhancement not only effectively elevates the detection performance of our method, but also ensures stable detection results. Experimental results on public datasets demonstrate that the proposed GANDPS outperforms multiple GAN-based methods in terms of detection precision, recall, and F1-score, and the detection results are stabilized within acceptable ranges. © 2023 IEEE.

关键词： Anomaly detection

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A Deep Dive into Task-Based parallelism in Python

A Deep Dive into Task-Based Parallelism in Python

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

作者： Ruys, William Lee, Hochan You, Bozhi Talati, Shreya Park, Jaeyoung Almgren-Bell, James Yan, Yineng Fernando, Milinda Biros, George Erez, Mattan Burtscher, Martin Rossbach, Christopher J. Pingali, Keshav Gligoric, Milos Univ Texas Austin Austin TX 78712 USA Texas State Univ San Marcos TX USA

ISBN: (纸本)9798350364613;9798350364606

Modern Python programs in high-performance computing call into compiled libraries and kernels for performance critical tasks. However, effectively parallelizing these finer-grained, and often dynamic, kernels across modern heterogeneous platforms remains a challenge. First, we perform an experimental study to examine the impact of Python's Global Interpreter Lock (GIL), and potential speedups under a GIL-less PEP703 future, to guide runtime design. Using our optimized runtime, we explore scheduling tasks with constraints that require resources across multiple, potentially diverse, devices through the introduction of new programming abstractions and runtime mechanisms. We extend an existing Python tasking library, Parla, to augment its performance and add support for such multi -device tasks. Our experimental analysis, using tasks graphs from synthetic and real applications, shows at least a 3x (and up to 6x) performance improvement over its predecessor in scenarios with high GIL contention. When scheduling multi-GPU tasks, we observe an Ax reduction in per-task launching overhead compared to a multi-process system.

关键词： task runtime parallel programming Python heterogeneous computing

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