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

共 24655 条记录，以下是281-290 订阅

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Early Exploration of Using ChatGPT for Log-based Anomaly Detection on parallel File Systems Logs 23

Early Exploration of Using ChatGPT for Log-based Anomaly Det...

引用

32nd international Symposium on High-Performance parallel and distributed computing (HPDC) part of the ACM Federated computing Research conference (FCRC)

作者： Egersdoerfer, Chris Zhang, Di Dai, Dong Univ N Carolina Charlotte NC 27599 USA

ISBN: (纸本)9798400701559

Log-based anomaly detection has been extensively studied to help detect complex runtime anomalies in production systems. However, existing techniques exhibit several common issues. First, they rely heavily on expert-labeled logs to discern anomalous behavior patterns. But labelling enough log data manually to effectively train deep neural networks may take too long. Second, they rely on numeric model prediction based on numeric vector input which causes model decisions to be largely non-interpretable by humans which further rules out targeted error correction. In recent years, we have witnessed groundbreaking advancements in large language models (LLMs) such as ChatGPT. These models have proven their ability to retain context and formulate insightful responses over entire conversations. They also present the ability to conduct few-shot and in-context learning with reasoning ability. In light of these abilities, it is only natural to explore their applicability in understanding log content and conducting anomaly classification among parallel file system logs.

关键词： anomaly detection parallel file system large language model

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An Efficient and Scalable Approach to Build Co-occurrence Matrix for DNN's Embedding Layer 24

An Efficient and Scalable Approach to Build Co-occurrence Ma...

引用

38th ACM international conference on Supercomputing (ACM ICS)

作者： Petit, Quentin Li, Chong Emad, Nahid Univ Paris Saclay Gif Sur Yvette France Huawei Technol France Gif Sur Yvette France Paris Distributed & Parallel Technol Lab Boulogne France Huawei Technol France Boulogne France Maison Simulat Gif Sur Yvette France LI PaRAD Gif Sur Yvette France

ISBN: (纸本)9798400706103

Embedding is a crucial step for deep neural networks. Datasets, from different applications, with different structures, can all be processed through an embedding layer and transformed into a dense matrix. The transformation must minimize both the loss of information and the redundancy of data. Extracting appropriate data features ensures the efficiency of the transformation. The co-occurrence matrix is an excellent way of representing the links between elements in a dataset. However, the dataset size becomes a problem in terms of computation power and memory footprint for using the co-occurrence matrix. In this paper, we propose a parallel and distributed approach to efficiently constructing the co-occurrence matrix in a scalable way. Our solution takes advantage of different features of boolean datasets to minimize the construction time of the co-occurrence matrix. Our experimental results show that our solution outperforms traditional approaches up to 34x. We also demonstrate the efficacy of our approach with a cost model.

关键词： Deep Learning distributed computing Embedding Sparse matrix Co-occurrence matrix Large-scale data analytics

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Optimizing Big Data Analytics Architecture for Edge computing Using Container Technology 30

Optimizing Big Data Analytics Architecture for Edge Computin...

引用

30th IEEE international conference on parallel and distributed Systems, ICPADS 2024

作者： Peng, Hong Bai, YunTian Kang, GuanYing Li, Yang Chen, TingWei LiaoNing University LiaoNing ChongShan Middle Road Huanggu District ShenYang China

ISBN: (纸本)9798331515966

This study is dedicated to the integration of big data analytics with edge computing, a critical need driven by the exponential growth of Internet of Things (IoT) technologies and smart device data. We introduce an optimized architecture that integrates Docker containerization with Apache Hadoop YARN, designed specifically for decentralized, resource-constrained edge environments. This innovative framework not only facilitates dynamic resource management but also ensures robust fault tolerance under the heterogeneous and often unstable network conditions typical of edge settings. By integrating Docker and Docker Swarm for container orchestration along with YARN for resource scheduling, our architecture significantly enhances data processing localization. This improvement reduces computational latency and boosts system performance. Through empirical evaluation, we demonstrate marked enhancements in task flexibility, resource efficiency, and operational continuity, particularly adept at managing the diversity and sporadic conditions of edge nodes. Additionally, the deployment of advanced load-balancing tools and multiple management nodes within containers enables the system to autonomously reallocate tasks during node failures or resource-constrained periods. Experimental results show that our architecture achieves an average failover recovery time of less than five seconds and maintains an average CPU utilization below 3% for management nodes, demonstrating significant advantages in handling large-scale IoT data at edge nodes. © 2024 IEEE.

关键词： Resource allocation

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parallel Runtime Interface for Fortran (PRIF): A Multi-Image Solution for LLVM Flang

Parallel Runtime Interface for Fortran (PRIF): A Multi-Image...

引用

2024 Workshops of the international conference for High Performance computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Bonachea, Dan Rasmussen, Katherine Richardson, Brad Rouson, Damian Computer Languages and Systems Software Group NERSC Lawrence Berkeley National Laboratory United States

ISBN: (纸本)9798350355543

Fortran compilers that provide support for Fortran's native parallel features often do so with a runtime library that depends on details of both the compiler implementation and the communication library, while others provide limited or no support at all. This paper introduces a new generalized interface that is both compiler- and runtime-library-agnostic, providing flexibility while fully supporting all of Fortran's parallel features. The parallel Runtime Interface for Fortran (PRIF) was developed to be portable across shared- and distributed-memory systems, with varying operating systems, toolchains and architectures. It achieves this by defining a set of Fortran procedures corresponding to each of the parallel features defined in the Fortran standard that may be invoked by a Fortran compiler and implemented by a runtime library. PRIF aims to be used as the solution for LLVM Flang to provide parallel Fortran support. This paper also briefly describes our PRIF prototype implementation: Caffeine. © 2024 IEEE.

关键词： Caffeine Fortran GASNet-EX HPC LLVM Flang parallel Fortran PGAS RMA Runtime Libraries

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Analysis of parallel Graph Applications 30

Analysis of Parallel Graph Applications

引用

30th IEEE international conference on parallel and distributed Systems, ICPADS 2024

作者： Atik, Funda Yesil, Serif Ouarnoughi, Hamza Niar, Smail Ozturk, Ozcan Washington University in St. Louis Computer Science and Engineering Department DoimukhMI United States NVidia UrbanaIL United States Université Polytechnique Hauts-de-France Department of Computer Science Valenciennes France LAMIH/CNRS Université Polytechnique Hauts-de-France Valenciennes France Sabanci University Computer Science and Engineering Program Electronics Engineering Program Istanbul Turkey

ISBN: (纸本)9798331515966

Despite the increasing computing power of shared memory systems with high core counts, parallel graph processing frameworks cannot exploit it effectively. The reason behind this is the inherent challenges in parallel graph algorithms, which are efficient management of dynamically created tasks and irregular data access patterns. In this paper, we categorize several popular design choices into three design dimensions: (i) execution mode, (ii) data access pattern, and (iii) work activation. We provide their high-level parallel implementations and analyze various implementations of three representative iterative graph algorithms by considering these design dimensions. To gain a better understanding of design choices, we examine their impacts on performance, communication, scalability, and work efficiency. We also investigate the communication characteristics of the design choices on two state-of-the-art shared-memory platforms by performing micro-architectural analysis. Our microarchitectural analysis reveals that a topology-driven, pull-based model gives up to 20 x better performance. © 2024 IEEE.

关键词： Communication Graph Analytics parallel Frameworks Performance Shared Memory Systems

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MLPs: Efficient Training of MiniGo on Large-scale Heterogeneous computing System 28

MLPs: Efficient Training of MiniGo on Large-scale Heterogene...

引用

IEEE 28th international conference on parallel and distributed Systems (IEEE ICPADS)

作者： Qiao, Peng He, Zhouyu Li, Rongchun Jiang, Jingfei Dou, Yong Li, Dongsheng Natl Univ Def Technol Sci & Technol Parallel & Distributed Lab Changsha Peoples R China

ISBN: (纸本)9781665473156

Deep Reinforcement Learning has been successfully applied in various applications and achieved impressive performance compared with previous traditional methods but suffers from high computation cost and long training time. MLPerf takes deep reinforcement learning as one of the benchmark tracks and provides a single node training version of MiniGo as a reference. A key challenge is to achieve efficient MiniGo training on a large-scale computing system. According to the training computation pattern in MiniGo and the characteristics of our large-scale heterogeneous computing system, we propose a MultiLevel parallel strategy, MLPs, including task-level parallelism between nodes, CPU-DSP heterogeneous parallelism, and DSP multi-core parallelism. The proposed method reduces the overall execution time from 43 hours to 16 hours while scaling the node size from 1067 to 4139. The scaling efficiency is 69.1%. According to our fitting method, the scaling efficiency is 46.5% when scaling to 8235 nodes. The experimental results show that the proposed method achieves the efficient training of MiniGo on the largescale heterogeneous computing system.

关键词： deep reinforcement learning deep neural networks MLPerf heterogeneous architecture large-scale parallel computing

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A Hybrid Multi-Threaded parallel Iterated Greedy Algorithm for distributed Flowshop Group Scheduling Problems with Preventive Maintenance 7

A Hybrid Multi-Threaded Parallel Iterated Greedy Algorithm f...

引用

7th international conference on Advanced Algorithms and Control Engineering, ICAACE 2024

作者： Sun, Xiaobin Sang, Hongyan Zhao, Yasheng Wu, Wanzhong School Of Computing LiaoCheng University Liaocheng China

ISBN: (纸本)9798350361445

The distributed flowshop group scheduling abstracted from the production process of printed circuit boards is a recent active research topic. The distributed flow shop group scheduling problem is based on the assumption that processing machines can run continuously. However, in actual production, factories not only pursue productivity, but also pay attention to the reliability and stability of the production process. In this paper, we study a distributed flowshop group scheduling problem with preventive maintenance (DFGSP/PM) to minimize the makespan. Based on the characteristics of DFGSP/PM, we proposed a hybrid multi-threaded parallel iterated greedy algorithm (HMPIG). The greedy NEH algorithm based on the LPT rule is used in the initialization phase of the algorithm to generate the initial solution. Different destruction and reconstruction rules for groups and jobs are designed to update the solution in the destruction and reconstruction phase. In the local search phase, a hybrid multithreaded parallel search strategy is introduced to improve the search efficiency of the IG algorithm so that the optimal insertion position of the group in the sequence can be searched more quickly. The numerous experimental results show that HMPIG has better solution quality as well as solution stability. © 2024 IEEE.

关键词： Preventive maintenance

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Algorithm and Hardware Co-Design for Efficient PMCMC Acceleration on FPGA

Algorithm and Hardware Co-Design for Efficient PMCMC Acceler...

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9th international conference on Signal and Image Processing (ICSIP)

作者： Wang, Fulong Peng, Shiyu Gong, Yuanhao Li, Minghan Liu, Shuanglong Hunan Normal Univ Coll Hunan Prov Key Lab Low Dimens Quantum Struct & Quantum Contr Key Lab Phys & Devices Postmoore Era Changsha Peoples R China

ISBN: (纸本)9798350350920

The particle Markov-chain Monte Carlo (PMCMC) method is a stochastic algorithm that combines Particle Filters (PFs) and Markov-chain Monte Carlo (MCMC) techniques. This approach is widely used in Bayesian inference for high-dimensional state spaces and nonlinear, non-Gaussian dynamic systems. However, current PMCMC accelerators face significant challenges due to their intensive computational complexity and the intricate particle routing, limiting their application in real-time scenarios. To address these challenges, we propose a novel distributed PMCMC method that leverages parallel computing to enhance hardware execution speed. Additionally, our method introduces a particle exchange scheme that not only resolves the accuracy issues caused by particle routing in distributed PMCMC but also achieves faster computing speed. Our design is implemented on a Xilinx Kintex-7 xc7k480t FPGA device. Experimental results demonstrate that our accelerator is nearly 65x faster than CPU performance, and provides speedups up to 5x compared to existing FPGA-based accelerators.

关键词： Particle Markov-chain Monte Carlo (PMCMC) Particle Filter (PF) Field Programmable Gate Arrays (FPGAs) distributed PMCMC Hardware Acceleration

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Innovations in Cloud and Edge computing Security Safeguarding Privacy in distributed Systems

Innovations in Cloud and Edge Computing Security Safeguardin...

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2024 international conference on Sustainable computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Jayanthi, Muthuswamy Suneel, Sajja Gupta, Manish Kansal, Vipashi Mallikarjuna Raju, K. Albawi, Ali Department of Electronics and Communication Engineering New Horizon College of Engineering Bangalore India Institute of Aeronautical Engineering Dundigal Hyderabad500043 India Lovely Professional University Phagwara India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of CSBS GRIET Bachupally Telangana Hyderabad India Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq

ISBN: (纸本)9798350355338

This work advances cloud and edge computing security, especially in distrib-uted systems. The suggested response has been extensively tested and shown to improve authentication, access control, hazard detection, and data integri-ty verification. Using ablation research, this report examines how the rec-ommended strategy works together. Machine learning-based threat identification and privacy protection appear crucial to system performance. The study provides minor security tips for dispersed populations. I like how the offered method balances data encryption and decryption speed with secure multi-party computing. This balance ensures the response is achievable and adapts to evolving computing settings. After comparison, the recommended distributed system security solution is standard. Its consistently above-average performance suggests it can establish industry norms. The study im-proves cloud and edge security and opens the door to greater research and cooperation, which will improve security approaches. This study improves distributed systems and highlights how vital privacy is in cloud and edge computing. © 2024 IEEE.

关键词： Cloud security

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Optimization of GraphQL Queries in WSO2 Platform 4

Optimization of GraphQL Queries in WSO2 Platform

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4th international conference on advances in Electrical, computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Ondero, Stanley Karanja, Mwangi Kaburu, Dennis Jomo Kenyatta University of Agriculture and Technology Computer Science Nairobi Kenya Jomo Kenyatta University of Agriculture and Technology Computing Science Nairobi Kenya

ISBN: (纸本)9798350343670

In the rapidly evolving landscape of application programming interfaces (APIs), GraphQL has emerged as a potent query language, known for its flexibility and efficiency. However, as the complexity of enterprise systems increases, the need to optimize GraphQL queries becomes paramount, especially within the context of the WSO2 API management ecosystem. This paper presents a comprehensive analysis and optimization strategies for GraphQL queries in distributed WSO2 API manager, aiming to enhance performance, reduce server load, and improve scalability. By leveraging leverages a combination of query analysis, caching mechanisms, query rewriting, and efficient data retrieval strategies, throughput improves by 5.9% and response times improves by 4.6%. It was as observed that java virtual machine optimization (JVM) achieves negligible API performance improvement. © 2024 IEEE.

关键词： GraphQL N+1 Problem Performance Query Optimization WSO2 API Management

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