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parallel and Batch Multiple Replica Auditing Protocol for Edge computing 21

Parallel and Batch Multiple Replica Auditing Protocol for Ed...

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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, Yi Zheng, Wenying Pandi, Vijayakumar Bhuiyan, Md Zakirul Alam Thamilarasi, C. Nanjing University of Information Science and Technology School of Computer Science Nanjing China Zhejiang Sci-Tech University School of Computer Science and Technology Hangzhou China University College of Engineering Tindivanam Anna University Chennai Department of Computer Science and Engineering Melpakkam India Fordham University Department of Computer and Information Sciences BronxNY United States P.S.V College of Enfgineering and Technoogy Department of Electronics and Communication Engineering Krishnagiri India

ISBN: (纸本)9798350329223

The edge computing architecture exposes data transmission and storage to unauthorized access, compromising data integrity. Users typically employ multiple data backups to ensure data reliability and availability, enhancing data redundancy and recovery capabilities. Nevertheless, verifying the integrity of multiple backup data under edge computing entails computational and communication overhead. In response, we present a parallel and batch multiple replica auditing protocol tailored for edge computing. Contributions include an efficient multiple replica construction method based on RSA, minimizing computational overhead, and a parallel data integrity verification approach that reduces communication overhead. A prototype implementation is evaluated in a real edge computing environment and compared to existing multiple replica auditing approaches. © 2023 IEEE.

关键词： Edge computing

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Modeling Power Consumption of Lossy Compressed I/O for Exascale HPC Systems 36

Modeling Power Consumption of Lossy Compressed I/O for Exasc...

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36th IEEE international parallel and distributed Processing symposium (IEEE IPDPS)

作者： Wilkins, Grant Calhoun, Jon C. Clemson Univ Holcombe Dept Elect & Comp Engn Clemson SC 29634 USA

ISBN: (纸本)9781665497473

Exascale computing enables unprecedented, detailed and coupled scientific simulations which generate data on the order of tens of petabytes. Due to large data volumes, lossy compressors become indispensable as they enable better compression ratios and runtime performance than lossless compressors. Moreover, as (high-performance computing) HPC systems grow larger, they draw power on the scale of tens of megawatts. Data motion is expensive in time and energy. Therefore, optimizing compressor and data I/O power usage is an important step in reducing energy consumption to meet sustainable computing goals and stay within limited power budgets. In this paper, we explore efficient power consumption gains for the SZ and ZFP lossy compressors and data writing on a cloud HPC system while varying the CPU frequency, scientific data sets, and system architecture. Using this power consumption data, we construct a power model for lossy compression and present a tuning methodology that reduces energy overhead of lossy compressors and data writing on HPC systems by 14.3% on average. We apply our model and find 6.5 kJs, or 13%, of savings on average for 512GB DO. Therefore, utilizing our model results in more energy efficient lossy data compression and DO.

关键词： Power demand Runtime Exascale computing Computational modeling Systems architecture Writing Compressors

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A parallel Acceleration Technique based on Bordered Block Diagonal Matrix Reordering for Exponential Integrator Method

A Parallel Acceleration Technique based on Bordered Block Di...

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2nd international symposium of Electronics Design Automation (ISEDA)

作者： Zhou, Hang Yang, Dongen Lin, Yangfei Dai, Yong Chen, Quan Southern Univ Sci & Technol Sch Microelect Shenzhen 518055 Peoples R China SMiT Grp Fuxin Technol Ltd Shenjiu Sci & Technol Venture Futian Dist Shenzhen Peoples R China

ISBN: (纸本)9798350352047;9798350352030

The exponential integrator (EI) method has proven to be an effective technique to accelerate transient circuit simulation. One core step of EI is the generation of rational Krylov subspace basis by the Arnoldi process, which involves one (exact) LU factorization and many back/forward triangular solves. Traditional parallelization techniques for circuit simulation perform well in accelerating the LU factorization part (the symbolic and numerical factorization) but fall short in parallelizing the triangular solution part. In this paper, we propose a parallel Bordered Block Diagonal (BBD) matrix reordering algorithm and a parallelization based on OpenMP and MPI to accelerate triangular solutions involved in the rational Krylov space construction in EI. parallelization is achieved by solving corresponding part of solution vector of each diagonal matrix block independently. Numerical experiments shows that our method can achieve 3.3x and 1.9x speedup under shared and distributed memory environment respectively.

关键词： EI(exponential integrator) BBD(bordered block diagonal) parallel computing Matrix Reordering

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Performance Evaluation of Data Transfer API for Rank Level Approximate computing on HPC Systems 36

Performance Evaluation of Data Transfer API for Rank Level A...

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36th IEEE international parallel and distributed Processing symposium (IEEE IPDPS)

作者： Morie, Yoshiyuki Wada, Yasutaka Kobayashi, Ryohei Sakamoto, Ryuichi Teikyo Univ Fac Fukuoka Med Technol Fukuoka Japan Meisei Univ Sch Informat Sci Tokyo Japan Univ Tsukuba Ctr Computat Sci Ibaraki Japan Tokyo Inst Technol Global Sci Informat & Comp Ctr Tokyo Japan

ISBN: (纸本)9781665497473

Approximate computing (AC) has attracted much attention to optimize tradeoffs among performance, power consumption, and computation results accuracy by adjusting data precision in applications. Even on HPC systems, AC is demanded to maximize performance under the limited power budget and hardware resources. To apply AC for HPC applications, we need to consider the character of each MPI rank in an application and optimize it with its appropriate data precision. However, we also need to perform data transfer while converting the precision of the target data. This paper proposes data pack/unpack APIs, which are applicable for standard MPI programs for HPC systems, for converting the data precision of the target data, and shows its performance evaluation. We can express data transfer among ranks with different data precision with the proposed APIs. The performance evaluation reveals the break-even point to apply AC for HPC applications from the perspective of data transfer volume.

关键词： Approximate computing Data Transfer API HPC Systems

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Benchmarking Quantum Processor Performance through Quantum Distance Metrics Over An Algorithm Suite 36

Benchmarking Quantum Processor Performance through Quantum D...

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36th IEEE international parallel and distributed Processing symposium (IEEE IPDPS)

作者： Stein, Samuel Wiebe, Nathan Ang, James Li, Ang Pacific Northwest Natl Lab Richland WA 99352 USA

ISBN: (纸本)9781665497473

Quantum computing is poised to solve computational paradigms that classical computing could never feasibly reach. Tasks such as prime factorization to Quantum Chemistry are examples of classically difficult problems that have analogous algorithms that are sped up on quantum computers. To attain this computational advantage, we must first traverse the noisy intermediate scale quantum (NISQ) era, in which quantum processors suffer from compounding noise factors that can lead to unreliable algorithm induction producing noisy results. We describe QASMBench, a suite of QASM-level (Quantum assembly language) benchmarks that challenge all realisable angles of quantum processor noise. We evaluate a large portion of these algorithms by performing density matrix tomography on 14 IBMQ Quantum devices.

关键词： Performance evaluation Computers distributed processing Program processors Quantum chemistry Conferences Benchmark testing

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Composing Pipeline parallelism using Control Taskflow Graph 22

Composing Pipeline Parallelism using Control Taskflow Graph

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31st international symposium on High-Performance parallel and distributed computing (HPDC)

作者： Chiu, Cheng-Hsiang Huang, Tsung-Wei Univ Utah Dept Elect & Comp Engn Salt Lake City UT 84112 USA

ISBN: (纸本)9781450391993

Graph-based propagation (GBP) is a common parallel pattern in many graph computing applications. Many GBP applications compose pipeline parallelism for each linear segment in the graph, where each task encapsulates a sequence of linearly dependent functions. This type of task-parallel pipeline parallelism is hard to express using mainstream programming frameworks (e.g., oneTBB) that count on data-parallel models to perform pipeline scheduling. In this paper, we introduce a new task-parallel method to compose pipeline parallelism in a GBP workload by leveraging the state-of-the-art control taskflow graph model. We demonstrate the promising performance of our method on a real circuit simulation workload.

关键词： parallel Pipeline

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GANs-based Signal Quality Assessment for Heart Rate Estimation with Ballistocardiograph 22

GANs-based Signal Quality Assessment for Heart Rate Estimati...

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22nd IEEE international symposium on parallel and distributed Processing with Applications, ISPA 2024

作者： Cai, Ruilin Qi, Jun Li, Jiayi Chen, Jianjun Wang, Wei Zhang, Haiyang Xi'an JiaoTong-Liverpool University Department of Computing Suzhou China

ISBN: (纸本)9798331509712

The ballistocardiograph (BCG) is a non-contact technology that monitors the heart and provides detailed cardiovascular parameters. Despite its broad applicability for long-term home monitoring due to Covid-19, BCG signals face challenges from positional changes, body movements, and system noise, which impact detection algorithms. In this paper, we propose a method for detecting inter-beat intervals (IBI) based on signal fusion technology. We utilize a Dynamic Bayesian Network (DBN) to integrate five heartbeat localization features extracted from BCG signals. Additionally, Generative Adversarial Networks (GANs) are used to assess signal quality and select correlated channels, improving heart rate monitoring accuracy. Experimental results demonstrate an average coverage of 95.21% and a mean squared error of 0.05. These results outperform those of methods without channel selection and single-channel BCG, indicating the potential for improving IBI estimation in multichannel BCG signal sensor systems. © 2024 IEEE.

关键词： Heart

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Enhanced Multi-Intent Recognition with BERT Embeddings and Graph-based Decoding 22

Enhanced Multi-Intent Recognition with BERT Embeddings and G...

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22nd IEEE international symposium on parallel and distributed Processing with Applications, ISPA 2024

作者： Cui, Mengmeng Zhang, Rui Xiang, Haolong Xue, Shengjun Nanjing University of Information Science and Technology Nanjing China Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Nanjing China Silicon Lake College Suzhou China

ISBN: (纸本)9798331509712

In the question-answering system, users frequently express multiple intents within a single utterance. The majority of intent recognition models tend to either primarily address single-intent scenarios or simply aggregate the overall intent context vectors of all tokens, neglecting the integration of multi-intent information. However, current methods suffer from slow inference speed, limited generalization capability, and the relatively independent treatment of multi-intent recognition tasks, leading to suboptimal model performance. In this paper, we design a novel model based on BERT, employing a non-autoregressive approach for the task of multi-intent recognition. This model achieves enhanced speed and accuracy. Additionally, this model introduces a global slot-intent interaction layer, simulating interactions between multiple intents and slots within an utterance. Experimental results on a series of benchmarking datasets demonstrate that this model outperforms the state-of-the-art methods, improving both the effectiveness and efficiency of the overall system. © 2024 IEEE.

关键词： Graph embeddings

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A Methodology to Build Decision Analysis Tools Applied to distributed Reinforcement Learning 36

A Methodology to Build Decision Analysis Tools Applied to Di...

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36th IEEE international parallel and distributed Processing symposium (IEEE IPDPS)

作者： Prigent, Cedric Cudennec, Loic Costan, Alexandru Antoniu, Gabriel Univ Rennes INRIA CNRS IRISA Rennes France DGA Maitrise Informat Rennes France

ISBN: (纸本)9781665497473

As Artificial Intelligence-based applications become more and more complex, speeding up the learning phase (which is typically computation-intensive) becomes more and more necessary. distributed machine learning (ML) appears adequate to address this problem. Unfortunately, ML also brings new development frameworks, methodologies and high-level programming languages that do not fit to the regular high-performance computing design flow. This paper introduces a methodology to build a decision making tool that allows ML experts to arbitrate between different frameworks and deployment configurations, in order to fulfill project objectives such as the accuracy of the resulting model, the computing speed or the energy consumption of the learning computation. The proposed methodology is applied to an industrial-grade case study in which reinforcement learning is used to train an autonomous steering model for a cargo airdrop system. Results are presented within a Pareto front that lets ML experts choose an appropriate solution, a framework and a deployment configuration, based on the current operational situation. While the proposed approach can effortlessly be applied to other machine learning problems, as for many decision making systems, the selected solutions involve a trade-off between several antagonist evaluation criteria and require experts from different domains to pick the most efficient solution from the short list. Nevertheless, this methodology speeds up the development process by clearly discarding, or, on the contrary, including combinations of frameworks and configurations, which has a significant impact for time and budget-constrained projects.

关键词： Machine Learning distributed computing Decision Making

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A semi-supervised GCN-based community detection algorithm 22

A semi-supervised GCN-based community detection algorithm

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22nd IEEE international symposium on parallel and distributed Processing with Applications, ISPA 2024

作者： Shi, Shu-Han Shi, Lei-Lei Liu, Lu Han, Zi-Xuan Jiang, Liang Chen, Fu-Xiang Jiangsu University School of Computer Science and Communication Engineering China University of Exeter Department of Computer Science United Kingdom University of Science and Technology School of Ocean Jiangsu China University of Leicester School of Computing and Mathematical Sciences United Kingdom

ISBN: (纸本)9798331509712

Community detection reveals the unique characteristics and relationships of in-network members, differentiated from out-of-community members and plays a pivotal role in network analysis. In recent years, deep learning techniques have made great strides in the application of community detection, especially on label sampling models, which train graph convolutional networks by constructing a balanced training set through structural centre localization and neighbourhood node expansion. However, such algorithms are limited on centre selection in community structure. To address this, this paper introduces a novel community detection algorithm based on peak density adaptive iterative segmentation, or LDACN in short. First, labels are assigned by adaptively selecting the centre node of the community structure, which results in a more even distribution of labels in the network. Subsequently, more accurate community segmentation is achieved by taking advantage of GCN's combined ability in capturing the connectivity relationships between the nodes and their intrinsic characteristics. Experimental results on synthetic and real-world network datasets show that our algorithm improves the effectiveness of community segmentation compared to the state-of-the-art algorithms. © 2024 IEEE.

关键词： Deep learning

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