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21st International Conference on Web Information Systems and Applications (WISA)

作者： Wang, Dong Ma, Ming Cheng Liu, Lingli Du, Xiongxiong Li, Xiaoruo Zhu, Bingnan Henan Univ Henan Int Joint Lab Intelligent Network Theory & Kaifeng 475000 Peoples R China Henan Univ Sch Software Kaifeng 475000 Peoples R China

ISBN: (纸本)9789819777068;9789819777075

Principal component analysis (PCA) is a widely used technique in the field of machine learning and one of the main dimensionality reduction methods. PCA can convert high-dimensional data into lower-dimensional representations, thereby helping to extract important information from data, and is an effective tool for data analysis and pattern recognition. In this paper, we propose a scheme for executing PCA on encrypted data based on homomorphic encryption in a distributed environment. In a distributed setting, computing nodes do not need to perform matrix operations such as matrix multiplication or addition, which reduces the computational burden on the nodes. To ensure the security of the data, we employ the CKKS homomorphic encryption scheme, which allows for approximate calculations on real numbers, meeting the requirements of machine learning. Additionally, we horizontally partition the data to facilitate its application in distributed computing. Furthermore, we optimize the detailed computation of HPCA (Homomorphic PCA), yielding promising results on various datasets.

关键词： Homomorphic Encryption distributed computing Principal Component Analysis (PCA) CKKS scheme Goldschmidt's Algorithm

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DeepDecompose: A distributed inference framework for CNN on GPU-equipped Edge Clusters 24

DeepDecompose: A Distributed inference framework for CNN on ...

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5th International Conference on computing, Networks and Internet of Things (CNIOT)

作者： Li, Lei Li, Yunlong Tan, Hu Yunnan Univ Kunming Yunnan Peoples R China

ISBN: (纸本)9798400716751

Edge computing is gaining traction due to its scalability benefits. However, deploying convolutional neural networks (CNNs) on resource-constrained IoT devices poses challenges for real-time performance. To address these issues, we propose DeepDecompose, a distributed inference framework for CNNs on GPU-equipped IoT clusters. DeepDecompose optimizes parallelism and GPU utilization to reduce inference latency. Deployed on Nvidia Jetson Nano, a GPU-equipped IoT device, DeepDecompose divides CNN models into smaller sub-models for independent inference, resulting in up to 2x faster performance compared to single-GPU computation.

关键词： Edge computing Deep learning distributed computing GPU

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Optimizing performance of parallel computing platforms for large-scale genome data analysis

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computing 2025年第3期107卷 1-22页

作者： Noor, Sumaiya Awan, Hamid Hussain Hashmi, Amber Sarwar Saeed, Aamir Khan, Salman Alqahtani, Salman A. Purdue Univ Business & Management Sci Dept W Lafayette IN USA Rawalpindi Women Univ Dept Comp Sci Rawalpindi Pakistan Univ Engn & Technol Dept Comp Sci & IT Jalozai Campus Peshawar Pakistan Abdul Wali Khan Univ Mardan Dept Comp Sci Mardan KPK Pakistan King Saud Univ Coll Comp & Informat Sci New Emerging Technol & 5G Network & Beyond Res Cha Dept Comp Engn Riyadh Saudi Arabia

With high-speed technological advancements and Next-Generation Sequencing (NGS), the discovered genome dataset (i.e., DNA/RNA/Protein) is increasing exponentially, magnifying every month. Timely analysis of the genome dataset is very important for understanding biological activities and drug development. However, due to the vast amounts of sequences and the complex structure of genome datasets, the storage and timely analysis of genome datasets is becoming challenging for traditional analysis techniques. distributed and cluster computing platforms are becoming significant for big data analytics and are now required in computational biology. This paper presents a computational model named Sprak-Pi-DNN, based on a parallel deep neural network for the timely classification of large RNA sequences into piRNAs and non-piRNAs. The proposed model takes advantage of parallel and distributed computing platforms. The performance of the proposed Sprak-Pi-DNN was extensively evaluated in two parts. In the first part, we compare and analyze the performance of two widely used cluster-based big data analytics platforms (Apache Hadoop and Apache Spark) on the same benchmark dataset. The computational-based metrics include computation times, speedup, and scalability. The second part assessed the proposed model's effectiveness using performance metrics such as accuracy, specificity, sensitivity, and Matthews's correlation coefficient. We employed the PseKNC algorithm for feature extraction with varied K-sizes. The experimental results revealed that Apache Spark's execution time performed better and faster than Apache Hadoop. Moreover, the evaluation results in both cases showed that the proposed model improved computation speedup without affecting accuracy.

关键词： Hadoop Big Data Spark Cluster computing distributed computing Bioinformatics Genome

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Thompson Sampling and Proportional-Greedy Algorithm for Uncertain Coded Edge computing

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第3期74卷 4865-4876页

作者： Kong, Linglin Sung, Chi Wan Shum, Kenneth W. City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China Chinese Univ Hong Kong Shenzhen Sch Sci & Engn Shenzhen 518172 Peoples R China

This paper investigates the allocation of coded computing tasks in edge networks with uncertain conditions. Specifically, it focuses on scenarios where the computing speed-related parameters of edge devices are heterogeneous and unknown. The challenge lies in mitigating the impact of stragglers, which involves identifying fast workers and distributing a reasonable workload among them. In this paper, we put the fast worker identification into a multi-armed bandit (MAB) framework and propose a Thompson sampling (TS)-based approach to tackle it. Then, our approach leverages the heterogeneity of computing speeds by formulating the task allocation problem to minimize the expected computing delay. We derive a lower bound for the delay and prove that our proposed algorithm minimizes this bound. Importantly, the time complexity of our algorithm is independent of the number of tasks to be assigned, which is typically large in practical scenarios. When our scheme is applied to solve the linear regression problem, simulation results show that it reduces the computing delay of a state-of-the-art method by more than 15% in two different scenarios.

关键词： Delays Vectors Computational modeling Edge computing Resource management Decoding Optimization Load modeling Computational efficiency Codes Coded edge computing distributed computing computation offloading straggler mitigation multi-armed bandits Thompson sampling

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Exploring data science workflows: A practice-oriented approach to teaching processing of massive datasets

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JOURNAL OF PARALLEL AND distributed computing 2025年 200卷

作者： Schoder, Johannes Buecker, H. Martin Friedrich Schiller Univ Jena Inst Comp Sci Ernst Abbe Pl 2 D-07743 Jena Germany Friedrich Schiller Univ Jena Michael Stifel Ctr Jena Data Driven & Simulat Sci Leutragraben 1 D-07743 Jena Germany

Massive datasets are typically processed by a sequence of different stages, comprising data acquisition and preparation, data processing, data analysis, result validation, and visualization. In conjunction, these stages form a data science workflow, a key element enabling the solution of data-intensive problems. The complexity and heterogeneity of these stages require a diverse set of techniques and skills. This article discusses a hands-on practice-oriented approach aiming to enable and motivate graduate students to engage with realistic data science workflows. A major goal of the approach is to bridge the gap between academia and industry by integrating programming assignments that implement different data workflows with real-world data. In consecutive assignments, students are exposed to the methodology of solving problems using big data frameworks and are required to implement different data workflows of varying complexity. This practice-oriented approach is well received by students, as confirmed by different surveys.

关键词： Big data Education Spark Hadoop Parallel computing distributed computing

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DiCE: distributed Code generation and Execution 22

DiCE: Distributed Code generation and Execution

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22nd IEEE Conference on Pervasive and Intelligent computing

作者： Rao, Kunal Coviello, Giuseppe Chakradhar, Srimat NEC Labs Amer Princeton NJ 08540 USA

ISBN: (纸本)9798331522759;9798331522742

Generative artificial intelligence (GenAI), specifically, Large Language Models (LLMs), have shown tremendous potential in automating several tasks and improving human productivity. Recent works have shown them to be quite useful in writing and summarizing text (articles, blogs, poems, stories, songs, etc.), answering questions, brainstorming ideas, and even writing code. Several LLMs have emerged specifically targeting code generation. Given a prompt, these LLMs can generate code in any desired programming language. Many tools like ChatGPT, CoPilot, CodeWhisperer, Cody, DeepSeek Coder, StarCoder, etc. are now routinely being used by software developers. However, most of the prior work in automatic code generation using LLMs is focused on obtaining "correct" and working code, and mainly runs on a single computer (serial code). In this paper, we take this to the next level, where LLMs are leveraged to generate code for execution on a distributed infrastructure. We propose a novel system called DiCE, which takes serial code as input and automatically generates distributed version of the code and efficiently executes it on a distributed setup. DiCE consists of two main components (a) LLM-based tool (Synthia) to understand dependencies in serial code and automatically generate distributed version of the code using specialized programming model and semantics, and (b) Runtime (Hermod) to understand the semantics in the distributed code and realize efficient execution on a cluster of machines (distributed infrastructure). DiCE currently focuses on visual programs synthesized by tools like ViperGPT [1] and VisReP [2] (serial code), automatically identifies higher-level task parallelism opportunities (e.g., parallel object detection), transforms the code to exploit the parallelism, and finally efficiently executes it on a cluster of machines. Through our experiments using 100 examples from the GQA dataset [3], we show that the serial codes generated by ViperGPT are successfu

关键词： Generative Artificial Intelligence (GenAI) Large Language Models (LLM) code generation code optimization parallel computing distributed computing distributed systems distributed runtime

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Discovering Candidates for Gene Network Expansion by distributed Volunteer computing 14

Discovering Candidates for Gene Network Expansion by Distrib...

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13th IEEE International Symposium on Parallel and distributed Processing with Applications

作者： Asnicar, Francesco Erculiani, Luca Galante, Francesca Gallo, Caterina Masera, Luca Morettin, Paolo Sella, Nadir Semeniuta, Stanislau Tolio, Thomas Malacarne, Giulia Engelen, Kristof Argentini, Andrea Cavecchia, Valter Moser, Claudio Blanzieri, Enrico Univ Trento DISI Via Sommar 9 Povo Trento Italy Fdn Edmund Mach CRI San Michele All Adige Italy Univ Ghent Dept Biochem Ghent Belgium Dept Med Prot Res VIB Ghent Belgium CNR IMEM Trento Italy

ISBN: (纸本)9781467379526

Our group has recently developed gene@home, a BOINC project that permits to search for candidate genes for the expansion of a gene regulatory network using gene expression data. The gene@home project adopts intensive variable-subsetting strategies enabled by the computational power provided by the volunteers who have joined the project by means of the BOINC client. Our project exploits the PC algorithm (Spirtes and Glymour, 1991) in an iterative way, for discovering putative causal relationships within each subset of variables. This paper presents our infrastructure, called TN-Grid, that is hosting the gene@home project. Gene@home implements a novel method for Network Expansion by Subsetting and Ranking Aggregation (NESRA), producing a list of genes that are candidates for the gene network expansion task. NESRA is an algorithm that has: 1) a ranking procedure that systematically subsets the variables;the subsetting is iterated several times and a ranked list of candidates is produced by counting the number of times a relationship is found;2) several ranking steps are executed with different values of the dimension of the subsets and with different number of iterations producing several ranked lists;3) the ranked lists are aggregated by using a state-of-the-art ranking aggregator. In our experimental results, we show that NESRA outperforms both the PC algorithm and its order-independent version called PC*. Evaluations and experiments are done by means of the gene@home project on a real gene regulatory network of the model plant Arabidopsis thaliana.

关键词： Volunteer computing distributed computing BOINC Bioinformatics Gene Network Expansion

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Towards a Scalable and Efficient PGAS-Based distributed OpenMP 1

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20th International Workshop on OpenMP (IWOMP)

作者： Shan, Baodi Araya-Polo, Mauricio Chapman, Barbara SUNY Stony Brook Stony Brook NY 11794 USA TotalEnergies EP Res & Technol US LLC Houston TX 77002 USA

ISBN: (数字)9783031725678

ISBN: (纸本)9783031725661;9783031725678

MPI+X has been the de facto standard for distributed memory parallel programming. It is widely used primarily as an explicit two-sided communication model, which often leads to complex and error-prone code. Alternatively, PGAS model utilizes efficient one-sided communication and more intuitive communication primitives. In this paper, we present a novel approach that integrates PGAS concepts into the OpenMP programming model, leveraging the LLVM compiler infrastructure and the GASNet-EX communication library. Our model addresses the complexity associated with traditional MPI+OpenMP programming models while ensuring excellent performance and scalability. We evaluate our approach using a set of micro-benchmarks and application kernels on two distinct platforms: Ookami from Stony Brook University and NERSC Perlmutter. The results demonstrate that DiOMP achieves superior bandwidth and lower latency compared to MPI+OpenMP, up to 25% higher bandwidth and down to 45% on latency. DiOMP offers a promising alternative to the traditionalMPI+OpenMP hybrid programmingmodel, towards providing a more productive and efficient way to develop high-performance parallel applications for distributed memory systems.

关键词： PGAS MPI OpenMP distributed computing

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A distributed Framework for Large-scale Protein-protein Interaction Data Analysis and Prediction Using MapReduce

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 160-172页

作者： Lun Hu Shicheng Yang Xin Luo Huaqiang Yuan Khaled Sedraoui MengChu Zhou School of Computer Science and Technology Dongguan University of TechnologyDongguan 523808China School of Computer Science and Technology Wuhan University of TechnologyWuhan 430070China Chongqing Engineering Research Center of Big Data Application for Smart Cities and Chongqing Key Laboratory of Big Data and Intelligent ComputingChongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of SciencesUrumqi 830000China Center of Research Excellence in Renewable Energy and Power Systems and the Department of Electrical and Computer EngineeringFaculty of EngineeringKing Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA

Protein-protein interactions are of great significance for human to understand the functional mechanisms of *** the rapid development of high-throughput genomic technologies,massive protein-protein interaction(PPI)data have been generated,making it very difficult to analyze them *** address this problem,this paper presents a distributed framework by reimplementing one of state-of-the-art algorithms,i.e.,CoFex,using *** do so,an in-depth analysis of its limitations is conducted from the perspectives of efficiency and memory consumption when applying it for large-scale PPI data analysis and *** solutions are then devised to overcome these *** particular,we adopt a novel tree-based data structure to reduce the heavy memory consumption caused by the huge sequence information of *** that,its procedure is modified by following the MapReduce framework to take the prediction task distributively.A series of extensive experiments have been conducted to evaluate the performance of our framework in terms of both efficiency and *** results well demonstrate that the proposed framework can considerably improve its computational efficiency by more than two orders of magnitude while retaining the same high accuracy.

关键词： distributed computing large-scale prediction machine learning MapReduce protein-protein interaction(PPI)

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Dynamic Edge/Cloud Resource Allocation for distributed Computation under Semi-Static Demands 59

Dynamic Edge/Cloud Resource Allocation for Distributed Compu...

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59th Annual IEEE International Conference on Communications (IEEE ICC)

作者： Sartzetakis, Ippokratis Pantazopoulos, Panagiotis Katsaros, Konstantinos V. Sourlas, Vasilis Varvarigos, Emmanouel

ISBN: (纸本)9781728190549

Edge computing is a recent paradigm where the processing takes place close to the data sources. It therefore reduces latency and saves bandwidth compared to traditional cloud computing. The latter can continue to play a supportive role. Edge-cloud computing provides benefits in many use cases including distributed computation algorithms, where the processing is divided into a number of tasks that are executed in parallel on different equipment. An important relevant challenge is to allocate the appropriate resources to process the data that are continuously generated from user devices. The issue becomes more complicated when we take into account the variations in the volume of the generated data as a function of time. In this paper we present a resource allocation algorithm for distributed computation with emphasis on machine learning algorithms. We consider that the resource requirements vary with time in a semi-static way that exhibits some daily pattern. We distinguish between periodic (expected) variations that occur during the day, and sporadic variations due to unexpected events. We propose an Integer Linear Programming algorithm to allocate the periodic resource requirements. To handle the non-periodic requirements, we consider a suitable prediction algorithm coupled with a reconfiguration algorithm that allocates the predicted required resources. Our results indicate that our proposal outperforms traditional allocation algorithms in terms of resource utilization, monetary cost and achieved accuracy.

关键词： cloud and edge computing distributed computing distributed machine learning resource allocation prediction dynamic

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