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检索条件"任意字段=International Conference on Parallel and Distributed Processing Techniques and Applications"

共 11336 条记录，以下是251-260 订阅

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OEDASA: Optimization Enabled Error-Diluted Approximate Systolic Array Design for an Image processing Application 37

OEDASA: Optimization Enabled Error-Diluted Approximate Systo...

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37th international conference on VLSI Design (VLSID) / 23rd international conference on Embedded Systems (ES)

作者： Devi, Dantu Nandini Kumar, Gandi Ajay Gowda, Bindu G. Rao, Madhav IIIT Bangalore Bangalore Karnataka India

ISBN: (纸本)9798350384406

Systolic Array (SA) architecture is a hardware accelerator for running Artificial Intelligence (AI) workloads. Although approximate computing offers hardware and performance benefits, it often sacrifices accuracy, limiting its application to error-resilient tasks. Many inexact multipliers in SA exhibit one-sided Error Distribution (ErD), resulting in significant accumulated errors. Approximate Computing proves valuable in error-resilient image processing applications, fostering research into various approximation techniques for enhanced hardware performance. This paper explores SA architecture with different configurations of approximate multipliers (AMs) featuring distinct ErDs. It employs a meta-heuristic optimization algorithm, Particle Swarm Optimization (PSO), for image smoothing tasks. The study delves into trade-offs between hardware acceleration and accuracy, offering insights for advancing approximate computing in SA-based AI workloads. The PSO-evolved SA configuration showcases a 3.05% performance improvement, 10.63% silicon footprint reduction, and 10.32% power savings compared to the exact SA. The PSO-derived SA structure also offers notable hardware gains when compared with other state-of-theart (SOTA) benchmark designs. Additionally, it demonstrates a better Structural Similarity Index (SSIM) compared to SA with one-sided error-distributed AMs. The proposed spatially optimized SAs with AMs of different ErDs represent a step towards reliable hardware design and establishing a nearly exact AI accelerator system. All design files and results are shared openly with the research and design community for easy adoption and further exploration.

关键词： Systolic Array Convolution Approximate Computing Image processing Particle Swarm Optimisation (PSO)

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Integrating interactive performance analysis in Jupyter Notebooks for parallel programming education

Integrating interactive performance analysis in Jupyter Note...

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

作者： Oden, Lena Noelp, Klaus Brauner, Philipp Univ Hagen Comp Engn Hagen Germany Rhein Westfal TH Aachen Human Comp Interact Ctr Aachen Germany

ISBN: (纸本)9798350364613;9798350364606

Understanding the performance behavior of parallel applications is important in many ways, but doing so is not easy. Most open source analysis tools are written for the command line. We are building on these proven tools to provide an interactive performance analysis experience within Jupyter Notebooks when developing parallel code with MPI, OpenMP, or both. Our solution makes it possible to measure the execution time, perform profiling and tracing, and visualize the results within the notebooks. For ease of use, it provides both a graphical JupyterLab extension and a C++ API. The JupyterLab extension shows a dialog where the user can select the type of analysis and its parameters. Internally, this tool uses Score -P, Scalasca, and Cube to generate profiling and tracing data. This tight integration gives students easy access to profiling tools and helps them better understand concepts such as benchmarking, scalability and performance bottlenecks. In addition to the technical development, the article presents hands-on exercises from our well-established parallel programming course. We conclude with a qualitative and quantitative evaluation with 19 students, which shows a positive effect of the tools on the students' perceived competence.

关键词： Jupyter parallel programming performance analysis interactive programming high perfonnance computing

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A parallel Framework for Efficiently Updating Graph Properties in Large Dynamic Networks 23

A Parallel Framework for Efficiently Updating Graph Properti...

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24th international conference on distributed Computing and Networking (ICDCN)

作者： Khanda, Arindam Das, Sajal K. Missouri Univ Sci & Technol Rolla MO 65409 USA

ISBN: (纸本)9781450397964

Graph queries on large networks leverage the stored graph properties to provide faster results. Since real-world graphs are mostly dynamic, i.e., the graph topology changes over time, the corresponding graph attributes also change over time. In certain situations, recompiling or updating earlier properties is necessary to maintain the accuracy of a response to a graph query. Here, we first propose a generic framework for developing parallel algorithms to update graph properties on large dynamic networks. We use our framework to develop algorithms for updating Single Source Shortest Path (SSSP) and Vertex Color. Then we propose applications of the developed algorithms in Unmanned Aerial Vehicle (UAV) based delivery systems under time-varying dynamics. Finally, we implement our SSSP and vertex color update algorithms for Nvidia GPU architecture and show empirically that the developed algorithms can update properties in large dynamic networks faster than the state-of-the-art techniques.

关键词： datasets neural networks gaze detection text tagging

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Comparative Analysis of RGB Imaging and Hyperspectral Imaging using Machine Learning techniques 15

Comparative Analysis of RGB Imaging and Hyperspectral Imagin...

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15th international conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Lanke, Neha P. Chandak, M.B. Shri Ramdeobaba College of Engineering and Management Nagpur India

ISBN: (纸本)9798331300579

Image processing is the class of methods for face recognition, object detection, image compression, classification, etc. Hyperspectral imaging is the parallel acquisition of spatial and spectral information from an image, making it a useful tool for many applications. It is similar to an RGB color image, but it has a lot more channels and thereby making it challenging to visualize by the human eye. This paper discussed the fundamentals and components of RGB imaging and hyperspectral imaging. We also discussed the challenges of using a hyperspectral imaging system and some comparative study results of RGB imaging and hyperspectral imaging for various application areas. We also discussed the role of machine learning techniques in RGB imaging and hyperspectral image analysis. We also discussed future challenges and the scope of hyperspectral imaging. Though hyperspectral imaging has certain limitations, it can be a powerful image analysis tool for many applications in the future. © Grenze Scientific Society, 2024.

关键词： RGB color model

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Economical Electricity Supplement for Mobile Collectors in Large-Scale Multitask WSNs 21

Economical Electricity Supplement for Mobile Collectors in L...

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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： Zhai, Xin Xu, Lijie Zhang, Jialei Wang, Kun Xu, Jia Xu, Bei Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing210023 China

ISBN: (纸本)9798350329223

For large-scale multitask wireless sensor networks (LSM-WSNs), the traditional data collection mode could suffer low energy-efficiency on data transmission, since the large-scale multitask scenarios could result in much higher packet collision probability, especially for harsh environments. Mobile data collection is an efficient data acquisition way to prolong network lifetime for LSM-WSNs. However, the mobile collectors could suffer electricity shortage problem, since the limited battery capacity of any mobile collector could not afford the energy consumption of its long-distance movement and massive data collection in large-scale multitask scenarios. Deploying wireless chargers to supplement the energy of mobile collectors is a feasible solution to electricity shortage problem, but will incur extra charger deployment cost. In this paper, we focus on the problem that how to optimize such charger deployment cost, which is NP-hard. By transforming it into minimum-cost submodular cover problem, we devise an efficient approximation algorithm with a provable approximation ratio. The extensive simulation results reveal that our solution always outperforms the other solutions under whatever configurations. © 2023 IEEE.

关键词： Wireless sensor networks

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parallel-EvoCluster: An Open-Source parallel Nature-Inspired Optimization Clustering 1

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10th Annual international conference on Information Management and Big Data (SIMBig)

作者： Alvarez-Mamani, Edwin Yarahuaman-Rojas, Milagros Huillca-Huallparimachi, Raul Pontificia Univ Catolica Peru Engn Dept Lima Peru Univ Nacl San Antonio Abad Cusco Dept Informat Cuzco Peru Univ Nacl Mayor San Marcos Fac Syst Engn & Informat Lima Peru

ISBN: (数字)9783031636165

ISBN: (纸本)9783031636158;9783031636165

Bio-inspired algorithms are a branch of Artificial Intelligence in which the behavior of natural systems is emulated to design non-deterministic heuristic methods for search, clustering, optimization, and simulation. Clustering algorithms search for patterns in the data and identify groups into clusters having similar features. The objective of this research is to improve the execution times of ten bio-inspired serial algorithms to solve clustering problems. To achieve our objective, we use parallelization techniques to reduce execution time by utilizing all processing resources. We proposed parallel-EvoCluster to parallelize ten algorithms through two parallelization approaches, such as shared-memory and distributed-memory architecture using Open Multi-processing and Message Passing Interface APIs respectively. Both approaches were performed using 24 CPU cores. The results show a significant improvement in runtimes of the parallel algorithms, parallelization with MPI on average is 10.14x times faster and the average parallelization with OMP is 9.05x times faster than the serial versions. The code is available at: https://***/win7/parallel EvoCluster.

关键词： Clustering parallel computer Bio-inspired Model island MPI OMP

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Enriching Big Data Intrusion Detection and Service Through Mapping and parallel Computation

Enriching Big Data Intrusion Detection and Service Through M...

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international conference on Artificial Intelligence: Theory and applications (AITA)

作者： Roy, Koyel Deshmukh, Rushali JSPM Rajarshi Shahu Coll Engn Pune Maharashtra India

ISBN: (纸本)9789819984756;9789819984763

Large-scale analysis of data requires a significant amount of computing power and resources, so the vast and flexible interpreting, preservation, and remote servers that cloud computing companies provide are very attractive. MongoDB is a widely used database management system for big data that is known for its fast performance and ability to execute activities within the MapReduce framework. The notion of big data relies on the conceptualization of resource collaboration, where applications and related infrastructure are frequently distributed among multiple users or organizations through isolation techniques such as virtualization. Big data is vulnerable to various types of assaults owing to its collaborative assets and extensive system software. The lack of efficacy and utility in improving the efficiency and privacy of big data is a significant challenge. The mission of securing big data is of utmost importance, as any discrepancies in this regard can have significant detrimental effects. The complex and voluminous characteristics of big data pose significant challenges in the application of traditional security methodologies, thereby leading to potential issues. Therefore, this research approach utilizes effective big data query classification and partitioning along with mapping and parallel computation for improving efficiency, as well as a bilinear pairing, avalanche effect, and tamper detection for forensic analysis and report generation. The approach has been effectively tested and compared with conventional approaches to achieve improved outcomes.

关键词： Data mapping Avalanche effect Bilinear pairing parallel computation Big data

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PreScienceMED: Leveraging Text Classification for Precise Disease Prediction 8

PreScienceMED: Leveraging Text Classification for Precise Di...

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8th IEEE international conference on distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024

作者： Prathwini Prathyakshini Nitte Department of Master of Computer Applications Karkala India Nitte Department of Information Science and Engineering Karkala India

ISBN: (数字)9798350350593

ISBN: (纸本)9798350350593

Emergent of new and modern technologies provide personalized solutions to health issues faced by the individuals. A chatbot using Natural language processing (NLP) is proposed which can detect relevant disease based on the user input. Different classification techniques such as Support vector Machine (SVM), Random Forest, Gaussian Naive Bayes and Decision Tree are used in detecting the relevant diseases with each disease containing 17 symptoms. Based on the symptoms the all the models can detect the diseases out of which Random Forest yields better accuracy score of 99.59%. Hence the proposed model can detect the diseases based on user queries. The study utilizes machine learning algorithms in order to automatically classify medical data based on the symptoms, reducing the reliance on manual analysis, and increasing efficiency. © 2024 IEEE.

关键词： Random forests

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Two-Phase distributed Algorithm for Solving the Bi-Objective Minimum Spanning Tree Problem: A Preliminary Study 15th

Two-Phase Distributed Algorithm for Solving the Bi-Objecti...

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15th international conference on parallel processing and Applied Mathematics, PPAM 2024

作者： Amorosi, Lavinia Cairo, Mariagrazia Dell’Olmo, Paolo Di Rocco, Lorenzo Ferraro Petrillo, Umberto Department of Statistical Sciences Università di Roma "La Sapienza" P.le Aldo Moro 5 Rome00185 Italy

ISBN: (纸本)9783031856990

In this paper we present a novel distributed algorithm for solving the Bi-Objective Minimum Spanning Tree (BMST) problem using a two-phase method. The proposed approach leverages the MapReduce computing paradigm to define a distributed version of the most computational intensive part of this method, the second phase. In this phase, a recursive algorithm explores non-supported non-dominated points by evaluating spanning trees within specified regions in the objective space. The distributed nature of our approach allows parallel processing of multiple triangles in the objective space aiming at reducing the execution time and improving the scalability performance. Our preliminary experimental results, conducted using an HPC infrastructure, show that the improvements in the scalability performance when tasks show a balanced processing time are non-negligible but also highlight that a simple distribution of processes among the nodes of the distributed system is not enough to always achieve a good parallelization. Our analysis provided us with useful insights to achieve efficiency and effectiveness of this approach with respect to its sequential counterpart, pointing out its potential for practical applications in multi-objective optimization. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： MapReduce

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O(N) distributed direct factorization of structured dense matrices using runtime systems 23

O(N) distributed direct factorization of structured dense ma...

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52nd international conference on parallel processing (ICPP)

作者： Deshmukh, Sameer Ma, Qinxiang Yokota, Rio Bosilca, George Tokyo Inst Technol Sch Comp Tokyo Japan Tokyo Inst Technol Global Sci Informat & Comp Ctr Tokyo Japan Univ Tennessee Innovat Comp Lab Knoxville TN USA

ISBN: (纸本)9798400708435

Structured dense matrices result from boundary integral problems in electrostatics and geostatistics, and also Schur complements in sparse preconditioners such as multi-frontal methods. Exploiting the structure of such matrices can reduce the time for dense direct factorization from O(N-3) to O(N). The Hierarchically Semi-Separable (HSS) matrix is one such low rank matrix format that can be factorized using a Cholesky-like algorithm called ULV factorization. The HSS-ULV algorithm is highly parallel because it removes the dependency on trailing sub-matrices at each HSS level. However, a key merge step that links two successive HSS levels remains a challenge for efficient parallelization. In this paper, we use an asynchronous runtime system PaRSEC with the HSS-ULV algorithm. We compare our work with STRUMPACK and LORAPO, both state-of-the-art implementations of dense direct low rank factorization, and achieve up to 2x better factorization time for matrices arising from a diverse set of applications on up to 128 nodes of Fugaku for similar or better accuracy for all the problems that we survey.

关键词： low rank approximation runtime systems HSS matrix distributed

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