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Implementing Asynchronous Jacobi Iteration on GPUs

Implementing Asynchronous Jacobi Iteration on GPUs

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Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (ScalA)

作者： Yu-Hsiang Mike Tsai Pratik Nayak Edmond Chow Hartwig Anzt Steinbuch Centre for Computing Karlsruhe Institute of Technology School of Computational Science and Engineering Georgia Institute of Technology Innovative Computing Laboratory University of Tennessee

ISBN: (纸本)9781665475716

Computation on architectures that feature fine-grained parallelism requires algorithms that overcome load imbalance, inefficient memory access, serialization, and excessive synchronization. In this paper, we explore an algorithm for iteratively solving systems of linear equations that allows for asynchronous updates by different execution units and completely removes the need for synchronization. Methods of this type have been identified as potentially competitive for computations on Exascale machines, but practical implementations for GPU platforms have scarcely been studied. We present an asynchronous Jacobi iteration optimized for high-end GPUs, demonstrate the superiority of the algorithm over a highly tuned synchronous Jacobi iteration, and deploy the algorithm as production-ready implementation in the Ginkgo open source library. The ideas presented here on the algorithm design, implementation and performance can help guide the design of other asynchronous iterative methods on GPUs.

关键词： Jacobian matrices Schedules Runtime Three-dimensional displays Software algorithms Graphics processing units Parallel processing

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Incorporating 5G NR Sidelink with MQTT to Maximize Energy Efficiency in IoT-Enabled Microgrids

Incorporating 5G NR Sidelink with MQTT to Maximize Energy Ef...

引用

Smart Power Control and Renewable Energy (ICSPCRE), IEEE International Conference on

作者： G Neelamegam Hermina JM Ashforn Mohamed S Raja J. Priyadharshini J Akshya Mani Deepak Choudhry M. Sundarrajan Department of Computer Science and Engineering VSB College of Engineering and Technology Coimbatore Department of Artificial Intelligence and Data Science Bannari Amman Institute of Technology Erode Department of Computer Science and Engineering Kalaignarkarunanidhi Institute of Technology Coimbatore Department of Information Technology K. Ramakrishnan College of Engineering Trichy Department of Computational Intelligence SRM Institute of Science and Technology Chennai Department of Computing Technologies SRM Institute of Science and Technology Chennai Department of Networking and Communications SRM Institute of Science and Technology Chennai

ISBN: (数字)9798350377002

ISBN: (纸本)9798350377019

IoT for smart microgrids is an innovative disruption at the cutting edge of energy management, relying on interconnectedness provided by smart sensors and devices to enhance power generation and distribution precision in metropolitan areas. The problems of the real-time operational capabilities required for efficient energy resource management thus progress, inhibit rapid control and management. The main goal of this research proposal consists of to use of the advanced technology of 5G NR Sidelink combined with MQTT to conduct a revolutionary communication system of Microgrid running through the IoT devices. The 5G NR (New Radio) Sidelink enables direct D2D communication, which leads to a dramatic lowering of the latency in the absence of traditional network infrastructures for communication, while the MQTT protocol improves the communication efficiency among networks of numerous IoT devices. The introduction of this system was followed by a 15% decrease in energy transmission losses and by 20% improvement in response times in comparison to conventional models specified in the quantitative evaluation thus increasing operational efficiency by far. These efficiencies not only reduce operating expenses and power consumption but also increase the solution's scalability, pointing out the possibility of building the solution for bigger urban microgrid implementations. In consideration of that, the suggested mechanism with 5G NR Sidelink along with MQTT overcomes elaborate barriers of IoT-based microgrids and establishes a new potency in advanced energy management in smart cities which could make the distribution network in the urban areas on the edge of the verge of a change.

关键词： Renewable energy sources 5G mobile communication Scalability Microgrids Sidelink Propagation losses Smart grids

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Improved Red Deer Algorithm for Scientific Workflow Scheduling in Cloud Environment 5

Improved Red Deer Algorithm for Scientific Workflow Scheduli...

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5th International Conference on Inventive Research in computing Applications, ICIRCA 2023

作者： Venkata Srinivas, Chandra Mouli Rena, Hajiyeva Arunarani, A.R. Naitik, S.T. Al Ansari, Mohammed Saleh Younas, Ammar Bonam Venkata Chalamayya Engineering College Andhra Pradesh Odalarevu India Azerbaijan Western Caspian University Department of Information Technologies Azerbaijan School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Computational Intelligence Tamilnadu Chennai India Dayananda Sagar University Cse Department Karnataka Bangalore India College of Engineering University of Bahrain Department of Chemical Engineering Bahrain Tashkent State University of Law Department of Business Law Uzbekistan

ISBN: (纸本)9798350321425

One of the most difficult problems in the technological sector is the versatility of cloud computing, which can offer a flexible and responsive infrastructure in the realm of information technology. A significant problem in cloud computing that is addressed in recent research is workflow scheduling. With the quick advancement of cloud computing, scheduling the intricate scientific process on the cloud has grown to be an incredibly difficult task. It has been identified that one of the key challenges to optimizing the performance of cloud computing is scheduling workflow tasks so that it is processed by the most effective cloud network resources. Meta-heuristic optimization algorithms are frequently utilized to find a solution to effective task scheduling because of the intricacy of the problem and the extent of the search space. This research suggests a unique Improved Red Deer Algorithm (IRDA) to shorten execution time subject to a set budget. The performance of the proposed algorithm is evaluated against conventional optimization algorithms by applying it to scientific workflows including Montage and Epigenomics. The experimental results reveal that the proposed algorithm works better than other compared methods in reducing workflow task execution time and cost. © 2023 IEEE.

关键词： Cloud computing

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Neural Networks for Fault diagnosis in Electrical Machine

Neural Networks for Fault diagnosis in Electrical Machine

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Raja, R. Vinston Raja, J. Gogula, Sreenivasulu Katiyar, Atul Thilagam, T. Bhagavan, V.S. Department of Computational Intelligence Faculty of Engineering and Technology School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai 603203 India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai 600062 India Department of Computer Science and Engineering (Data Science) Vardhaman College of Engineering Telangana Hyderabad 501218 India Department of Electrical Engineering Mahatma Jyotiba Phule Rohilkhand University Uttar Pradesh Bareilly 243006 India Department of Science and Engineering S.A Engineering College Tamil Nadu Chennai 600077 India Department of Mathematics Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram 522302 India

ISBN: (纸本)9798350374933

Finding electrical equipment problems quickly is essential for minimising downtime and ensuring ongoing functioning. When it comes to early defect identification, traditional methods like human inspection or sensor-based surveillance might not cut it. Another alternative to employing machine learning algorithms to analyse complex patterns in sensor data is neural networks. With an emphasis on motor drives, this article lays forth a method for identifying electrical equipment issues using neural networks. The network has been trained to identify various issues, such as imbalanced rotors, broken stator windings, and failing bearings, by examining historical data. The trial findings prove that the suggested method may effectively find issues, make things more reliable, and cut down on maintenance expenses. © 2024 IEEE.

关键词： bearing wear electrical machines fault diagnosis machine learning motor drives Neural networks predictive maintenance rotor imbalance stator winding faults

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UAV -Based Object Tracking Using Siamese Networks and Kalman Filtering

UAV -Based Object Tracking Using Siamese Networks and Kalman...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： S.V Ramanan M. Rajasekar G. Shanmugapriya Sujitha R Akshya. J Mani Deepak Choudhry Department of Electronics and Communication Engineering P PG Institute of Technology Coimbatore Department of Computer Science and Engineering VSB college of Engineering Technical Campus Coimbatore Department of Computer Science and Business Systems Dr.N G.P Institute of Technology Coimbatore Department of Information Technology Christ The King Engineering College Coimbatore Department of Computational Intelligence SRM Institute of Science and Technology Chennai Department of Computing Technologies SRM Institute of Science and Technology Chennai

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

The ability to match features and keep track of objects in changing dynamic environments is still an important problem, particularly due to varying noise levels, diverse datasets, and high dimensional feature spaces. Euclidean Distance and Cosine Similarity based matching are existing methods that commonly do not generalize well, particularly in the case of cross domain scenarios, where noise and motion dynamics are powerful factors to limit the accuracy of tracking. However, Deep Metric Learning approaches can extract robust features at the cost of computational efficiency and are unsuitable for real-time applications. This research proposes a Siamese Network framework to solve these problems by using robust feature matching and multi-object tracking. Based on that, the proposed framework uses a dual-branch network architecture in which feature embeddings extracted from paired inputs are mapped through the same shared latent space. Furthermore, the adaptive learning rate leads to efficient convergence during the training. Experimental results show that the Siamese Network not only achieves better accuracy than the existing methods and the more such as Deep Metric Learning but also performs significantly more efficient computation. With an accuracy of 90.2 % at a precision of 88.5%, the framework's inference time is much faster than that of Deep Metric Learning at 2.0ms. Furthermore, the proposed method performs well on the generalization over datasets with a 15% improvement of cross-domain matching accuracy on Euclidean Distance. By striking this balance between accuracy, efficiency and adaptability, it shows its suitability for dynamic and noisy environments for real-time tracking. The proposed method fills existing gaps and achieves a new state of the art for feature matching and tracking performance.

关键词： Accuracy Dynamics Euclidean distance Feature extraction Real-time systems Robustness Object tracking Noise measurement Vehicle dynamics Noise level

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FPGA based Power Efficient Approximate 4:2 Compressor for Multimedia Applications 2

FPGA based Power Efficient Approximate 4:2 Compressor for Mu...

引用

2nd International Conference on Edge computing and Applications, ICECAA 2023

作者： Dinesh, G. Arunkumar, K. Radhakrishnan, P. Jadhav, Rajendra D. Dasari, Twinkle Lal, Hasamukh School of Computing Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Saveetha Engineering College Department of Ece Chennai India Tagore Engineering College Department of Ece Chennai India Institute of Management Kolhapur India Koneru Lakshmaiah Education Foundation Department of Engineering English Andhra Pradesh Vaddeswaram India Ies University Department of Agriculture Sciences M.P Bhopal India

ISBN: (纸本)9798350347579

Approximate computing plays a crucial part in error resilent and multimedia applications. This study presents the design and analysis of a 4:2 compressor that employs a Dadda multiplier to reduce output power and inaccuracy. The approximate compressors are utilized to design 8∗8, 16∗16,32∗32 multipliers. The goal of this work is to develop a compressor with low latency, low power consumption, and fewer product stages. By reducing the number of transistors and gates the low latency compressors used to serve as multiplier design applications. The adders and product stages are to be considerable power consumption in multiplication process. The reduced compressors consume less power and it could be applicable for designers. © 2023 IEEE.

关键词： Compressors

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AI-Enhanced Healthcare: Densenet Based Alzheimer's Disease Prediction and Online Consultation for Accessible Medical Support

AI-Enhanced Healthcare: Densenet Based Alzheimer's Disease P...

引用

Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Wilfred Blessing N. R Hemalatha Gunasekaran Hariharan B. Jaber Saleh Salim Al Jabri Kavitha S. J Sutherlin Subitha G. College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Sultanate of Oman Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Department of Computer Science and Engineering Stella Mary's College of Engineering Aruthenganvilai Tamil Nadu India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Improvements in patient care and early disease detection possibilities are two of the ways artificial intelligence is transforming the healthcare industry. Alzheimer's disease, a neurodegenerative disease that worsens with time, is among the most difficult diseases to exactly identify in early stages. Patients thus delay in treatment and experience a decline in their quality of life. Research is still under progress, thus knowledge about how AI-driven prediction models could be combined with readily available healthcare services remains lacking. This work presents a special artificial intelligence-powered system consisting of DenseNet-based deep learning for Alzheimer's disease prediction with an online consultation platform. The proposed approach analyzes brain MRI data and classifies Alzheimer's stages with higher accuracy using a DenseNet architecture, well-known for its exceptional performance in the field of feature extraction. Comprising three thousand magnetic resonance imaging (MRI) images, the dataset has been preprocessed for noise reduction and tuned for models performance. At 98.5%, the DenseNet model exceeded other methods including VGGNet and ResNet in accuracy.

关键词： Accuracy Magnetic resonance imaging Redundancy Medical services Predictive models Brain modeling Real-time systems Alzheimer's disease Medical diagnostic imaging Overfitting

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Artificial Intelligence in Health Care computing and Assessments in Oman: an Effectual Study

Artificial Intelligence in Health Care Computing and Assessm...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Health care plays a vital responsibility in a human life span. World countries targets to provide better care for their citizens. Digital transformations and innovations in Artificial Intelligence (AI) technologies helps in deep for the novel solutions to speed-up the decision making process in health management and patient consulting. AI branches including Machine Learning (ML), Deep Learning (DL), Neural Networks (NN), Natural Language Processing (NLP) and so forth can support innovations in heal care domain. This paper presents the comprehensive and noble review on recent existing tools and techniques and also recommends advanced techniques for health care industry that are to be used in Sultanate of Oman. Furthermore, overcoming AI challenges are resulted and presented through the study. This work mainly aimed to produce the multiple dimensions and challenges that helps to develop AI powered real-life applications in health care support system.

关键词： Deep learning Technological innovation Ethics Reviews Decision making Surgery Medical services Natural language processing Medical diagnostic imaging Diseases

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Blood Cancer Detection and Classification using Auto Encoder and Regularized Extreme Learning Machine 8

Blood Cancer Detection and Classification using Auto Encoder...

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8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Kanimozhi, N. Nayak, Swikruti Keshav Kumar, K. Manjramkar, Vinda Kumar, Ranjeet Suganthi, D. Srm Institute of Science and Technology School of Computing Department of Computational Intelligence Chennai Tamilnadu Kattankulathur India Jecrc University Department of Computer Science and Engineering Rajasthan Jaipur India Department of Humanities and Mathematics Shaikpet Telangana Hyderabad India Zoology Maharashtra Thane India University College of Engineering & Technology Vinoba Bhave University Department of Electronics & Communication Jharkhand Hazaribagh India Saveetha Institute of Medical and Technical Sciences Saveetha College of Liberal Arts and Sciences Department of Computer Science Thandalam Chennai India

ISBN: (纸本)9798350396638

Leukaemia is considered as the most common type of blood cancer. The length of time one requires to recover from this disease is determined by how quickly it is recognized and treated. Leukaemia develops when the bone marrow generates an abnormally large amount of white blood cells. Since images of human blood samples are studied under the microscope, haematologists employ techniques such as microscopic colour imaging, image segmentation, grouping, and classification to diagnose patients as fast as possible. Microscopy may identify blood cancer in visible and immature white blood cells in a variety of methods. Early and accurate diagnosis of leukaemia is critical for clinicians to properly treat patients. After the input image is provided, the gaussian filter is used to perform image enhancement, and the Wiener filter is used in image Deblurring, Adaptive K-Means clustering technique, Colour feature, Texture feature, GLCM and Hausdroff are also used, and lastly the Model is trained using AE and RELM. The proposed system outperforms conventional models such as RELM and ELM, which generates an accuracy of 98.45%. © 2023 IEEE.

关键词： Image enhancement

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Cloud Based Smart Medical Recommendation System Using Pattern Recognition

Cloud Based Smart Medical Recommendation System Using Patter...

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Networking and Communications (ICNWC), International Conference on

作者： E. Sujatha S. Shanthasheela P.V. Gopirajan Department of Computer Science and Engineering Saveetha Engineering College (Autonomous)Thandalam Chennai Department of Computer Science and Engineering Velammal Institute of Technology Chennai Department of Computational Intelligence School of Computing SRM Institute of Science and TechnologyKattankulathur Chennai

To build smart medical recommendation system the data needs to be collected from laboratories. The system is intended to provide services as control and manage data samples, industry standards, real test results, reports, human resources in the laboratories, machines, tiny instruments, and automation of the workflow. Combining the data from the laboratories and medical institutions enable ease of communication without time delay. The challenging task is that identifying appropriate hardware and software components which provides compatibility in collecting data and their processing steps, repository storage, secured distribution, and the policies used for information representation in the results. In order to automate the entire process of medical recommendation system the metric parameters are used for determining the efficiency and productivity of the lab to enhance the quality of control systems. The vulnerability lies in maintaining traditional way of records in paper and error in the data. The purpose of this project is to develop the process of daily tasks will be computed through the database using pattern recognition techniques.

关键词： Productivity Measurement Laboratories Process control Software Pattern recognition Task analysis

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