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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161-170 订阅

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Implementation of Blockchain Technology to Enhance Data Security for Cloud computing 1

Implementation of Blockchain Technology to Enhance Data Secu...

引用

1st IEEE international conference on advances in computing, Communication and Networking, ICAC2N 2024

作者： Kumari, Poonam Singh, Meeta Manav Rachna International Institute of Research and Studies Department of Computer Science and Engineering Haryana Faridabad India

ISBN: (纸本)9798350356816

Cloud computing, the fastest-growing IT technology, offers flexibility and scalability with pay-per-use models, but it raises concerns about data security due to third-party storage and online accessibility. Effective cloud security measures are crucial for protecting user data. The Internet of Things (IoT) generates enormous amounts of data that must be efficiently processed and stored in the cloud. Blockchain technology enhances data security through its tamper-resistant distributed ledger and peer-to-peer networks, which allow any node with internet access to join or create networks. To protect sensitive data and maintain its integrity, it is crucial to implement robust encryption, privacy safeguards, access controls, identity management and confidentiality in both cloud and decentralized systems. © 2024 IEEE.

关键词： Cloud security

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Task-based low-rank hybrid parallel Cholesky factorization for distributed memory environment 24

Task-based low-rank hybrid parallel Cholesky factorization f...

引用

7th international conference on High Performance computing in Asia-Pacific Region (HPC Asia)

作者： Jiao, Han Zhang, Jilin Suzuki, Tomohiro Univ Yamanashi Integrated Grad Sch Med Engn & Agr Sci Kofu Yamanashi Japan Hangzhou Dianzi Univ Sch Comp Sci Hangzhou Zhejiang Peoples R China Univ Yamanashi Div Engn Dept Interdisciplinary Reseach Kofu Yamanashi Japan

ISBN: (纸本)9798400708893

The primary targets for improving efficiency for large-scale matrix factorization are reducing synchronization, addressing the overlap in communication and computation, and improving load balance. In recent years, tiled algorithms with task parallelism in multicore shared memory systems have become well-established as efficient methods for conducting fine-grained computations on smaller tiles. Moreover, they provide flexible execution orders for a runtime system in many situations. However, traditional hybrid programs with MPI and OpenMP for distributed memory systems use a fork-join model for multi-threads in each process, which leads to thread-parallel computing tasks interchange with sequential communication tasks. In this paper, we incorporate task parallelism and low-rank approximation into a hybrid task-based Cholesky factorization in a distributed environment and propose some low-rank variants. We evaluate the performance of our programs on both full-rank inputs and low-rank inputs and report the pros and cons of the proposed programs.

关键词： matrix factorization task parallelism MPI plus OpenMP low-rank approximation

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TAPS: A Performance Evaluation Suite for Task-based Execution Frameworks 20

TAPS: A Performance Evaluation Suite for Task-based Executio...

引用

20th IEEE international conference on E-Science (E-Science)

作者： Pauloski, J. Gregory Hayot-Sasson, Valerie Gonthier, Maxime Hudson, Nathaniel Pan, Haochen Zhou, Sicheng Foster, Ian Chard, Kyle Univ Chicago Dept Comp Sci Chicago IL 60637 USA Argonne Natl Lab Data Sci & Learning Div Lemont IL 60439 USA

ISBN: (纸本)9798350365627;9798350365610

Task-based execution frameworks, such as parallel programming libraries, computational workflow systems, and function-as-a-service platforms, enable the composition of distinct tasks into a single, unified application designed to achieve a computational goal and abstract the parallel and distributed execution of those tasks on arbitrary hardware. Research into these task executors has accelerated as computational sciences increasingly need to take advantage of parallel compute and/or heterogeneous hardware. However, the lack of evaluation standards makes it challenging to compare and contrast novel systems against existing implementations. Here, we introduce TAPS, the Task Performance Suite, to support continued research in distributed task executor frameworks. TAPS provides (1) a unified, modular interface for writing and evaluating applications using arbitrary execution frameworks and data management systems and (2) an initial set of reference synthetic and real-world science applications. We discuss how the design of TAPS supports the reliable evaluation of frameworks and demonstrate TAPS through a survey of benchmarks using the provided reference applications.

关键词： Performance Evaluation parallel computing Open-Source Python Workflows

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UNR: Unified Notifiable RMA Library for HPC 24

UNR: Unified Notifiable RMA Library for HPC

引用

2024 international conference for High Performance computing, Networking, Storage and Analysis

作者： Feng, Guangnan Xie, Jiabin Dong, Dezun Lu, Yutong Sun Yat Sen Univ Guangzhou Peoples R China Natl Key Lab Parallel & Distributed Comp Changsha Peoples R China

ISBN: (数字)9798350352917

ISBN: (纸本)9798350352924;9798350352917

Remote Memory Access (RMA) enables direct access to remote memory to achieve high performance for HPC applications. However, most modern parallel programming models lack schemes for the remote process to detect the completion of RMA operations. Many previous works have proposed programming models and extensions to notify the communication peer, but they did not solve the multi-NIC aggregation, portability, hardware-software co-design, and usability problems. In this work, we proposed a Unified Notifiable RMA (UNR) library for HPC to address these challenges. In addition, we demonstrate the best practice of utilizing UNR within a real-world scientific application, PowerLLEL. We deployed UNR across four HPC systems, each with a different interconnect. The results show that PowerLLEL powered by UNR achieves up to a 36% acceleration on 1728 nodes of the Tianhe-Xingyi supercomputing system.

关键词： Remote Memory Access Communication Optimization Synchronization parallel Programming Model MPI

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distributed Machine Learning 24

Distributed Machine Learning

引用

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

作者： Chatterjee, Bapi Iiit New Delhi Delhi India

ISBN: (纸本)9798400716737

We explore the landscape of distributed machine learning, focusing on advancements, challenges, and potential future directions in this rapidly evolving field. We delve into the motivation for distributed machine learning, its essential techniques, real-world applications, and open research questions. The theoretical discussion will give an overview of proving the convergence of popular Stochastic Gradient Descent (SGD) Algorithms to train contemporary machine learning models, including the deep learning models with the assumption of non-convexity, in a distributed setting. We will specify the convergence of data parallel SGD for various distributed systems properties, such as asynchronous and compressed communication. We will also discuss distributed machine learning techniques such as model parallelism and tensor parallelism to train large language models (LLMs). © 2024 ACM.

关键词： Stochastic systems

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parallelize Hashcat Password Cracking using MPI 5

Parallelize Hashcat Password Cracking using MPI

引用

5th international conference on Computer Engineering and Application (ICCEA)

作者： Xiao, Guojian Li, Kuan Yin, Jianping Wang, Weicheng Dongguan Univ Technol Sch Comp Sci & Technol Dongguan Guangdong Peoples R China

ISBN: (纸本)9798350386783;9798350386776

Given the contemporary complexities within the realm of digital forensics, distributed computing emerges as an imperative for effective password cracking. When confined to exclusively utilizing open-source software for password-cracking endeavors, it becomes evident that hashcat stands out unequivocally due to its exceptional speed and an extensive repertoire of supported hash formats. Traditionally, Hashcat is able to support a distributed hash-cracking system via overlays. In the paper, we show how to make hashcat parallel intuitively by introducing a message-passing interface (MPI) and provide a working solution for performing different cracking attacks. Experimental results of multiple cracking tasks demonstrate that the proposed approach is effective.

关键词： Hashcat MPI parallel Password Cracking

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Rethinking Low-Carbon Edge computing System Design with Renewable Energy Sharing 24

Rethinking Low-Carbon Edge Computing System Design with Rene...

引用

53rd international conference on parallel Processing (ICPP)

作者： Liao, Hanlong Tang, Guoming Guo, Deke Wang, Yi Cao, Ruide Natl Univ Def Technol Changsha Peoples R China Southern Univ Sci & Technol Peng Cheng Lab Heyuan Bay Area Digital Econ Technol Innovat Ctr Shenzhen Peoples R China Southern Univ Sci & Technol Shenzhen Peoples R China

ISBN: (纸本)9798400717932

The geographically distributed edge servers can naturally draw power from nearby renewable energy (RE) generators. Complemented by the dynamic scheduling of energy storage batteries, edge service providers (ESPs) can thus build low- or even zero-carbon edge computing systems. Nevertheless, the distributed and heterogeneous nature of edge computing systems, as well as the limited information sharing among ESPs, leads to a more complex battery planning problem than that in cloud computing. The unpredictability of RE resources further complicates the problem, making conventional model-based approaches ineffective. To this end, we propose a multi-agent deep reinforcement learning (MADRL) approach for the independent decision making of individual ESPs. Particularly, MADRL takes privacy into account by ensuring that no sensitive information is disclosed among ESPs. For better model training, we further customize the invalid action masking and develop action transformation techniques based on segmented linear optimization. Extensive experiments demonstrate that, with our proposed approach, the overall carbon emission of edge computing systems can be significantly reduced (by over 60%) while maintaining acceptable operation costs in battery scheduling.

关键词： Low-carbon edge computing renewable energy sharing multi-agent deep reinforcement learning

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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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High Altitude Platform Station (HAPS)-Enabled parallel computing for Handoff Control in Vehicular Networks

High Altitude Platform Station (HAPS)-Enabled Parallel Compu...

引用

IEEE international conference on Communications (IEEE ICC)

作者： Ren, Qiqi Abbasi, Omid Kurt, Gunes Karabulut Yanikomeroglu, Halim Chen, Jian Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Carleton Univ Dept Syst & Comp Engn Ottawa ON K1S 5B6 Canada Polytech Montreal Dept Elect Engn Montreal PQ H3T 1J4 Canada

ISBN: (纸本)9781538674628

distributed computing enables Internet of vehicle (IoV) services by collaboratively utilizing the computing resources from the network edge and the vehicles. However, the computing interruption issue caused by frequent edge network handoffs, and a severe shortage of computing resources are two problems in providing IoV services. High altitude platform station (HAPS) computing can be a promising addition to existing distributed computing frameworks due to its wide coverage and strong computational capabilities. In this regard, this paper proposes an adaptive scheme in a new distributed computing framework that involves HAPS computing to deal with the two problems of the IoV. Based on the diverse demands of vehicles, network dynamics, and the time-sensitivity of handoffs, the proposed scheme flexibly divides each task into three parts and assigns them to the vehicle, roadside units (RSUs), and a HAPS to perform synchronous computing. The proposed scheme also constrains the computing of tasks at RSUs such that they are completed before handoffs to avoid the risk of computing interruptions. We formulate a delay minimization problem that considers task-splitting ratio, transmit power, bandwidth allocation, and computing resource allocation. To solve the problem, variable replacement and successive convex approximation-based methods are proposed. The simulation results show that this scheme not only avoids the negative effects caused by handoffs in a flexible manner but also it improves the delay performance and maintains the delay stability.

关键词： Internet of vehicles (IoV) High altitude platform station (HAPS) distributed computing edge network handoff

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Synergy: Collaborating Centralized and Local Scheduling for Serverless Functions 30

Synergy: Collaborating Centralized and Local Scheduling for ...

引用

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

作者： Chen, Hanmei Zhao, Laiping Yang, Yanan You, Jianing Li, Keqiu Tianjin University Tianjin Key Lab. of Advanced Networking Tianjin China Tianjin University Tanklab Tianjin China Cloud Computing Research Institute China Telecom Beijing China

ISBN: (纸本)9798331515966

Serverless computing enables a new way of building and scaling cloud applications by allowing developers to write fine-grained functions. The execution duration of a cloud function is typically short, usually ranging from a few milliseconds to a few seconds. FaaS providers charge users based on the execution duration of cloud functions with a granularity of 1 millisecond. Existing mixed scheduling methods collocate functions with varying execution times, which may prolong their execution duration and lead to unfair charges for FaaS users. To address this problem, we propose a partition scheduling approach, placing functions with varying execution times on different partitions. We introduce Synergy, a solution in serverless computing that leverages a collaboration of central and local scheduling to partition functions and employs suitable scheduling algorithms for these partitions respectively. Synergy also supports the dynamic switching of scheduling algorithms for partitioned nodes to adapt to highly fluctuating loads. We evaluate Synergy using real-world, representative benchmarks. Experimental results demonstrate that, compared to state-of-the-art and conventional approaches, Synergy can reduce the average function execution duration by 63%. © 2024 IEEE.

关键词： Scheduling algorithms

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