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共 24468 条记录，以下是281-290 订阅

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Optimizing Transitive Closure Computation for High Performance computing and Security 33

Optimizing Transitive Closure Computation for High Performan...

引用

33rd Euromicro international conference on parallel, distributed, and Network-Based Processing, PDP 2025

作者： Masciari, Elio Napolitano, Enea Vincenzo DIETI University of Naples Federico II Naples Italy

ISBN: (纸本)9798331524937

Transitive closure computation is a fundamental operation in graph theory with applications in various domains. However, the increasing size and complexity of real-world graphs make traditional algorithms inefficient, especially when dealing with large datasets. This paper investigates the optimisation of transitive closure algorithms for high performance computing (HPC) applications. We implement and compare three different methods for computing the adjacency matrix of the transitive closure, based on three different Python libraries (NetworkX, PyTorch and NumPy). Our approach is benchmarked on seven real-world datasets of varying size and density to evaluate performance and scalability. The results show that NumPy achieves the best performance for large and dense graphs. The paper concludes with a discussion of the potential benefits of algorithmic optimization in HPC and security. © 2025 IEEE.

关键词： Graph algorithms

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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...

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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...

引用

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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Research on Cloud computing Security Based on Virtualization Security Technology 4

Research on Cloud Computing Security Based on Virtualization...

引用

4th international conference on Information Science, parallel and distributed Systems, ISPDS 2023

作者： Dang, Fangfang Yang, Ying Liu, Han Li, Dingding State Grid Henan Information & Telecommunication Company Henan Zhengzhou China

ISBN: (纸本)9798350337181

In order to overcome the problems of high intrusion rate and low encryption depth of traditional cloud computing security research methods, this paper proposes a new cloud computing security research method based on Virtualization security technology. First, analyze the principle of virtualization technology, and build a cloud computing resource transmission model to understand the whole cloud computing process. Secondly, the chaotic mapping method is used to transform cloud computing data to obtain binary chaotic sequence data, and realize the security encryption of cloud computing data to further ensure the security of cloud computing data. Finally, build the cloud computing data encryption and decryption algorithm to complete the cloud computing security research. Experimental results show that compared with traditional methods, the proposed method can effectively improve the security of cloud computing and reduce the intrusion rate. © 2023 IEEE.

关键词： Cryptography

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NavCim: Comprehensive Design Space Exploration for Analog computing-in-Memory Architectures 24

NavCim: Comprehensive Design Space Exploration for Analog Co...

引用

international conference on parallel Architectures and Compilation Techniques (PACT)

作者： Park, Juseong Kim, Boseok Sung, Hyojin Pohang Univ Sci & Technol Pohang South Korea Seoul Natl Univ Seoul South Korea

ISBN: (数字)9798400706318

ISBN: (纸本)9798400706318

Analog computing-in-memory (ACiM) technology has shown strong potential for neural network accelerators, addressing von-Neumann performance bottlenecks with in-memory data processing and computation. Understanding the ACiM design space, including its trade-offs and constraints, and systematically and effectively exploring it for optimal performance is essential to turn the promise into a viable product. Recent research demonstrated that multi-objective searches for ACiM architectures with heterogeneous tiles can simultaneously optimize power, performance, and area (PPA), outperforming existing tiled ACiM proposals. In this paper, we propose NavCim, a comprehensive ACiM design space exploration mechanism that advances the prior work in terms of search efficiency, search space coverage, and optimization metrics. NavCim introduces predictive modeling of ACiM hardware performance and uses the PPA prediction models instead of running simulators, significantly reducing search overheads. Faster searches enable NavCim to extend the architecture and model search spaces with an evolutionary search process to optimize architectures with more than two different tile sizes for multiple input models. With accuracy-aware searches, NavCim considers PPA and model accuracy together as optimization goals to achieve more balanced trade-offs. The experimental searches show that NavCim leverages predictive models to reduce search time by up to 7.3x without compromising the quality of search results. It also successfully identifies heterogeneous ACiM architectures that can efficiently execute multiple models on a single chip, improving accuracy by up to 19% over the prior work.

关键词： Accuracy Neural networks Predictive models Extraterrestrial measurements Data processing Hardware Space exploration parallel architectures Proposals Optimization

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distributed Exact Structural Clustering on Large Graph 28

Distributed Exact Structural Clustering on Large Graph

引用

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

作者： Zhou, Jinyu Rong, Chuitian Liu, Ding Chai, Zhengyi Tiangong Univ Tianjin Peoples R China

ISBN: (纸本)9781665473156

Graph clustering is an important technique to detect community clusters in complex networks. SCAN (Structural Clustering Algorithm for Networks) is a well-studied graph clustering algorithm that has been widely applied over the years. However, the processing time cost of sequential SCAN and its variants cannot be tolerable on large graphs. The existing parallel variants of SCAN are focusing on fully utilizing the computing capacity of multi-core computer architectures and inventing sophisticated optimization techniques on single computing node. As the objects and their relationships in cyberspace are varying over time, the scale of graph data is increasing with high rate. The graph clustering algorithms on single node are facing challenges from limited computing resources, such as computing performance, memory size and storage volume. The distributed processing algorithm is called for processing large graphs. This work presents a distributed structural graph clustering algorithm using Spark. Furthermore, the edge pruning technique and adaptive checking are optimized to improve clustering efficiency. And the label propagation clustering is simplified to reduce the communication cost in the distributed clustering iterations. It also conduct extensive experiments on real-world datasets to testify the efficiency and scalability of the distributed algorithm. Experimental results show that efficient clustering performance can be achieved and it scales well under different settings.

关键词： Structural Clustering distributed computing Spark

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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...

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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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