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

共 24639 条记录，以下是281-290 订阅

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Resource-Limited Localized Adaptive Adversarial Training for Machine Learning Model 21

Resource-Limited Localized Adaptive Adversarial Training for...

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： Rajhi, Mohammed Pissinou, Niki Florida International University School of Computing and Information Sciences MiamiFL United States Jazan University Faculty of Computer Science & Information Technology Jazan Saudi Arabia

ISBN: (纸本)9798350329223

Balancing robustness and computational efficiency in machine learning models is challenging, especially in settings with limited resources like mobile and IoT devices. This study introduces Adaptive and Localized Adversarial Training (ALAT), an optimization approach that balances these competing needs. ALAT combines generalized models with localized adversarial perturbations and adaptive data augmentation. As a result, the model strengthens its weak points without needing to explore all possible adversarial threats, saving computational effort Our data shows that ALAT-trained models perform robustly with less computational cost compared to traditional adversarial training methods. This adaptability makes ALAT suitable for various machine learning architectures and particularly valuable in resource-constrained settings requiring resilience to adversarial threats. © 2023 IEEE.

关键词： Computational efficiency

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HM2: Efficient Host Memory Management for RDMA-Enabled distributed Systems 23

HM2: Efficient Host Memory Management for RDMA-Enabled Distr...

引用

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

作者： Tang, Chen Chu, Zhaole Jin, Peiquan Luo, Yongping Guo, Kuankuan Univ Sci Technol China Hefei Peoples R China ByteDance Inc Beijing Peoples R China

ISBN: (纸本)9798400701559

Remote direct memory access (RDMA) supports zero-copy networking by transferring data from clients directly to host memory, eliminating the need to copy data between clients' memory and the data buffers in the hosting server. However, the hosting server must design efficient memory management schemes to handle incoming clients' data. In this paper, we propose a high-performance host memory management scheme called HM2 for RDMA-enabled distributed systems. We present a new buffer structure for incoming data from clients. In addition, we propose efficient data processing methods to reduce network transfers between clients and servers. We conducted a preliminary experiment to evaluate HM2, and the results showed HM2 achieved higher throughput than existing schemes, including L5 and FaRM.

关键词： RDMA memory management data buffer

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GraVAC: Adaptive Compression for Communication-Efficient distributed DL Training 16

GraVAC: Adaptive Compression for Communication-Efficient Dis...

引用

IEEE 16th international conference on Cloud computing (IEEE CLOUD)

作者： Tyagi, Sahil Swany, Martin Indiana Univ Bloomington IN 47405 USA

ISBN: (纸本)9798350304817

distributed data-parallel (DDP) training improves overall application throughput as multiple devices train on a subset of data and aggregate updates to produce a globally shared model. The periodic synchronization at each iteration incurs considerable overhead, exacerbated by the increasing size and complexity of state-of-the-art neural networks. Although many gradient compression techniques propose to reduce communication cost, the ideal compression factor that leads to maximum speedup or minimum data exchange remains an open-ended problem since it varies with the quality of compression, model size and structure, hardware, network topology and bandwidth. We propose GraVAC, a framework to dynamically adjust compression factor throughout training by evaluating model progress and assessing gradient information loss associated with compression. GraVAC works in an online, black-box manner without any prior assumptions about a model or its hyperparameters, while achieving the same or better accuracy than dense SGD (i.e., no compression) in the same number of iterations/epochs. As opposed to using a static compression factor, GraVAC reduces end-to-end training time for ResNet101, VGG16 and LSTM by 4.32x, 1.95x and 6.67x respectively. Compared to other adaptive schemes, our framework provides 1.94x to 5.63x overall speedup.

关键词： deep learning data-parallel training gradient compression sparsification adaptive systems

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A Unified Graph Framework for Storage-Compute Coupled Cluster and High-Density computing Cluster

A Unified Graph Framework for Storage-Compute Coupled Cluste...

引用

2024 international Workshop on Big Data in Emergent distributed Environments, BIDEDE 2024, in conjunction with the 2024 ACM SIGMOD/PODS conference

作者： Lin, Lynsey Chen, Jamie Sun, Ricky Zhang, Jason Wang, Victor Ultipa Inc United States

ISBN: (纸本)9798400706790

In this paper, we present a novel unified framework that seamlessly integrates distributed computing and high-density graph computing. Our approach leverages a hybrid architecture that combines the strengths of both paradigms, enabling efficient graph traversal and computation while ensuring scalability and flexibility. The key contributions of our work include: 1. A distributed graph storage and partitioning strategy that maximizes data locality and minimizes cross-machine communication overhead. 2. A high-density graph computing cluster optimized for memory-efficient operations and parallel execution. 3. Query optimization, include partition pruning, network optimization, parallel processing, and predicate pushdown. 4. Two approaches for implementing Pregel for graph-wide algorithms: a shard-based SCC (Storage-Compute Coupling) implementation and an HDC (High-Density computing) based implementation. Our unified framework addresses the critical challenges faced by contemporary graph databases and analytics frameworks, providing a robust and versatile solution tailored to the diverse requirements of modern graph-based applications. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Graph Databases

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Methods of increasing data reliability based on distributed and parallel technologies based on blockchain

Methods of increasing data reliability based on distributed ...

引用

international conference on Artificial Intelligence, Blockchain, computing and Security, ICABCS 2023

作者： Makhmadiyarovich, Nazarov Fayzullo Sherzodjon, Yarmatov Samarkand State University named after Sharof Rashidov Samarkand Uzbekistan

ISBN: (纸本)9781032669663

This article examines directions and mechanisms for increasing data reliability in computer networks. Currently, the rapid development of information technologies, the rapid growth of data flow, high-quality data processing carried out in network technologies, and the increase in the volume of data lead to an increase in the problem of data reliability. It is an urgent issue to find solutions based on the use of modern technologies to solve these problems. The simultaneous processing of various types of data in information systems, video, audio, text and digital data, creates big data. The variety of data types in bigdata creates the problem of quality data processing, which greatly affects the reliability of the data. Research shows that breaches of data integrity mainly manifest in three directions. In this case, there is a violation of the reliability of interrelated data in data transmission and storage, in the processing of large volumes of data and in the transcription of video data. It is created due to errors created during data transmission based on artificial and natural redundancy. To solve the mentioned problems, increasing data reliability based on blockchain mechanisms for payment systems in data transmission, increasing data reliability based on error minimization mechanisms in video information systems, and distributed computing and parallel mechanisms in large-scale information systems based on methods of increasing data reliability are researched. © 2024, CRC Press/Balkema. All rights reserved.

关键词： Information systems

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A Cloud-Agnostic Serverless Architecture for distributed Machine Learning 11

A Cloud-Agnostic Serverless Architecture for Distributed Mac...

引用

11th IEEE/ACM international conference on Big Data computing, Applications and Technologies, BDCAT 2024

作者： Predoaia, Ionut Garcia-Lopez, Pedro University of York United Kingdom Universitat Rovira i Virgili Spain

ISBN: (纸本)9798350367300

Serverless computing has shown vast potential for big data analytics applications, especially involving machine learning algorithms. Nevertheless, little consideration has been given in the literature to cloud-agnostic serverless architectures that leverage existing parallel implementations of machine learning algorithms. This work bridges this gap by proposing a multicloud serverless architecture for distributed machine learning, that enables machine learning engineers without cloud computing expertise to effortlessly port already implemented parallel machine learning algorithms to serverless, whilst overcoming vendor lock-in. In this work, two stateful machine learning algorithms have been ported to serverless, k-means clustering and logistic regression. The serverless implementation of k-means provided superior performance and scalability compared to a serverful implementation when using a number of workers that is equal to or slightly lower than the total number of vCPUs available on the VM running the serverful implementation. Additionally, it achieved an 87-fold speedup compared to a sequential implementation. Moreover, two storage designs of the shared state will be proposed for the serverless implementations, one that requires locks for updating the shared state, and another that is lock-free. Our experimental evaluation demonstrates that the performance of the lock-free serverless implementation of k-means declines with the increase in the number of clusters. © 2024 IEEE.

关键词： Adversarial machine learning

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distributed Multi-GPU Community Detection on Exascale computing Platforms

Distributed Multi-GPU Community Detection on Exascale Comput...

引用

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

作者： Sattar, Naw Safrin Lu, Hao Wang, Feiyi Halappanavar, Mahantesh Oak Ridge Natl Lab Oak Ridge TN 37830 USA Pacific Northwest Natl Lab Richland WA 99352 USA

ISBN: (纸本)9798350364613;9798350364606

Community detection is a fundamental operation in graph mining, and by uncovering hidden structures and patterns within complex systems it helps solve fundamental problems pertaining to social networks, such as information diffusion, epidemics, and recommender systems. Scaling graph algorithms for massive networks becomes challenging on modern distributed-memory multi-GPU (Graphics Processing Unit) systems due to limitations such as irregular memory access patterns, load imbalances, higher communication-computation ratios, and cross-platform support. We present a novel algorithm HiPDPL-GPU (distributed parallel Louvain) to address these challenges. We conduct experiments involving different partitioning techniques to achieve an optimized performance of HiPDPL-GPU on the two largest supercomputers: Frontier and Summit. Remarkably, HiPDPL-GPU processes a graph with 4.2 billion edges in less than 3 minutes using 1024 GPUs. Qualitatively, the performance of HiPDPL-GPU is similar or better compared to other state-of-the-art CPU- and GPU-based implementations. While prior GPU implementations have predominantly employed CUDA, our first-of-its-kind implementation for community detection is cross-platform, accommodating both AMD and NVIDIA GPUs.

关键词： multi-GPU Louvain community detection clustering hybrid HIP MPI

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PROPACK: Executing Concurrent Serverless Functions Faster and Cheaper 23

PROPACK: Executing Concurrent Serverless Functions Faster an...

引用

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

作者： Basu, Rohan Patel, Tirthak Liew, Richmond Babuji, Yadu Nand Chard, Ryan Tiwari, Devesh Northeastern Univ Boston MA 02115 USA Rice Univ Houston TX 77251 USA Argonne Natl Lab Argonne IL 60439 USA

ISBN: (纸本)9798400701559

The serverless computing model has been on the rise in recent years due to a lower barrier to entry and elastic scalability. However, our experimental evidence suggests that multiple serverless computing platforms suffer from serious performance inefficiencies when a high number of concurrent function instances are invoked, which is a desirable capability for parallel applications. To mitigate this challenge, this paper introduces ProPack, a novel solution that provides higher performance and yields cost savings for end users running applications with high concurrency. ProPack leverages insights obtained from experimental study to build a simple and effective analytical model that mitigates the scalability bottleneck. Our evaluation on multiple serverless platforms including AWS Lambda and Google confirms that ProPack can improve average performance by 85% and save cost by 66%. ProPack provides significant improvement (over 50%) over the state-of-the-art serverless workload manager such as Pywren, and is also, effective at mitigating the concurrency bottleneck for FuncX, a recent on-premise serverless execution platform for parallel applications.

关键词： Serverless computing Cloud computing Scalability

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Performance Optimization of distributed Machine Learning Algorithms Based on Cloud computing in Big Data Analysis

Performance Optimization of Distributed Machine Learning Alg...

引用

2024 international conference on Telecommunications and Power Electronics, TELEPE 2024

作者： Xie, Mengyi Quanzhou University of Information Engineering Fujian Quanzhou362000 China

ISBN: (纸本)9798350369212

Big data technology is increasingly penetrating various industries, bringing unprecedented opportunities to enterprises and society with its powerful data processing and analysis capabilities. At the same time, the rapid growth of cloud computing technology and artificial intelligence (AI) technology has also provided strong support for the widespread application of big data technology. Under the joint promotion of these technologies, various fields have ushered in unprecedented changes and progress. In recent years, with the rapid progress of AI technology, the application of distributed machine learning (DML) algorithms has become increasingly widespread. However, with the continuous expansion of data scale and the increasing demand for computing, research on improving the performance of DML algorithms has become increasingly urgent. To address this challenge, this article proposes a performance optimization strategy for cloud based DML algorithm in big data analysis. By fully utilizing the powerful computing and storage capabilities of cloud computing platforms, as well as the parallel processing advantages of DML algorithms, we aim to achieve efficient analysis and processing of large-scale data. The experimental results show that this method performs well in big data analysis, significantly improving the speed and accuracy of data processing. © 2024 IEEE.

关键词： Cloud platforms

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Towards Content-Based Image Retrieval for Encrypted Images over Cloud computing: Review of Recent Trends

Towards Content-Based Image Retrieval for Encrypted Images o...

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2023 international Telecommunications conference, ITC-Egypt 2023

作者： Elsayd, Rowayda A. Rashad, Metwally El-Attar, Noha E. Elsawy, Ahmed Benha University Faculty of Computers and Artificial Intelligence Department of Computer Science Benha Egypt Benha University Faculty of Computers and Artificial Intelligence Department of Information Systems Benha Egypt Information Technology Department Faculty of Technological Industry and Energy Delta University Egypt

ISBN: (纸本)9798350326062

Recent advances in imaging and computing technology generate tremendous image data daily. Searching image collections has been made easier with the introduction of some content-based image retrieval (CBIR) approaches. The research area of CBIR is currently an active topic. It is becoming increasingly important to process and store big data in a parallel and distributed manner using CBIR, especially cloud computing platforms. Cloud computing provides high amounts of computation power that can be accessed at low costs. However, cloud servers are not entirely secure, and data owners raise privacy concerns;thus, the encrypted image and encrypted feature are uploaded to the cloud server to ensure privacy. This paper presents the essential techniques of secure content-based image retrieval over cloud computing. We will compare and contrast several approaches for CBIR, especially with cloud computing. © 2023 IEEE.

关键词： Generative adversarial networks

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