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Static Analysis Techniques for Fixing Software Defects in MPI-Based Parallel Programs

computers, Materials & Continua 2024年第5期79卷 3139-3173页

作者： Norah Abdullah Al-Johany Sanaa Abdullah Sharaf Fathy Elbouraey Eassa Reem Abdulaziz Alnanih Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Software Engineering and Distributed System Research Group King Abdulaziz UniversityJeddah21589Saudi Arabia

The Message Passing Interface (MPI) is a widely accepted standard for parallel computing on distributed ***, MPI implementations can contain defects that impact the reliability and performance of parallelapplications. Detecting and correcting these defects is crucial, yet there is a lack of published models specificallydesigned for correctingMPI defects. To address this, we propose a model for detecting and correcting MPI defects(DC_MPI), which aims to detect and correct defects in various types of MPI communication, including blockingpoint-to-point (BPTP), nonblocking point-to-point (NBPTP), and collective communication (CC). The defectsaddressed by the DC_MPI model include illegal MPI calls, deadlocks (DL), race conditions (RC), and messagemismatches (MM). To assess the effectiveness of the DC_MPI model, we performed experiments on a datasetconsisting of 40 MPI codes. The results indicate that the model achieved a detection rate of 37 out of 40 codes,resulting in an overall detection accuracy of 92.5%. Additionally, the execution duration of the DC_MPI modelranged from 0.81 to 1.36 s. These findings show that the DC_MPI model is useful in detecting and correctingdefects in MPI implementations, thereby enhancing the reliability and performance of parallel applications. TheDC_MPImodel fills an important research gap and provides a valuable tool for improving the quality ofMPI-basedparallel computing systems.

关键词： high-performance computing parallel computing software engineering software defect message passing interface deadlock

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Energy efficient resource controller for Apache Storm

Energy efficient resource controller for Apache Storm

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作者： HoseinyFarahabady, MohammadReza Taheri, Javid Zomaya, Albert Y. Tari, Zahir Center for Distributed and High Performance Computing School of Computer Science The University of Sydney CamperdownNSW Australia Department of Computer Science Karlstad University Karlstad Sweden School of Computing Technologies RMIT University MelbourneVIC Australia

Apache Storm is a distributed processing engine that can reliably process unbounded streams of data for real-time applications. While recent research activities mostly focused on devising a resource allocation and task scheduling algorithm to satisfy high performance or low latency requirements of Storm applications across a distributed and multi-core system, finding a solution that can optimize the energy consumption of running applications remains an important research question to be further explored. In this article, we present a controlling strategy for CPU throttling that continuously optimize the level of consumed energy of a Storm platform by adjusting the voltage and frequency of the CPU cores while running the assigned tasks under latency constraints defined by the end-users. The experimental results running over a Storm cluster with 4 physical nodes (total 24 cores) validates the effectiveness of proposed solution when running multiple compute-intensive operations. In particular, the proposed controller can keep the latency of analytic tasks, in terms of 99th latency percentile, within the quality of service requirement specified by the end-user while reducing the total energy consumption by 18% on average across the entire Storm platform. © 2021 John Wiley & Sons, Ltd.

关键词： Storms

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AsT: An Asymmetric-Sensitive Transformer for Osteonecrosis of the Femoral Head Detection 37

AsT: An Asymmetric-Sensitive Transformer for Osteonecrosis o...

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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Chen, Haoyang Liu, Shuai Lu, Feng Li, Wei Sheng, Bin Li, Mi Jin, Hai Zomaya, Albert Y. National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Centre for Distributed and High Performance Computing School of Computer Science The University of Sydney Australia Department of Computer Science and Engineering Shanghai Jiao Tong University China Tongji Hospital Tongji Medical College Huazhong University of Science and Technology China

ISBN: (纸本)9781577358800

Early diagnosis of osteonecrosis of the femoral head (ONFH) can inhibit the progression and improve femoral head preservation. The radiograph difference between early ONFH and healthy ones is not apparent to the naked eye. It is also hard to produce a large dataset to train the classification model. In this paper, we propose Asymmetric-Sensitive Transformer (AsT) to capture the uneven development of the bilateral femoral head to enable robust ONFH detection. Our ONFH detection is realized using the self-attention mechanism to femoral head regions while conferring sensitivity to the uneven development by the attention-shared transformer. The real-world experiment studies show that AsT achieves the best performance of AUC 0.9313 in the early diagnosis of ONFH and can find out misdiagnosis cases firmly. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Large dataset

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Artificial Intelligence based prediction system for General Medicine 4

Artificial Intelligence based prediction system for General ...

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4th International Conference on computing, Mathematics and Engineering Technologies, iCoMET 2023

作者： Nadeem, Ghalib Rehman, Yawar Khaliq, Abdul Khalid, Huma Anis, Muhammad Irfan Neduet Department of Electronic Enginnering Karachi Pakistan Iqra University Department of Computer Science Karachi Pakistan College of Pharmacy Lumhs Jamshoro Pakistan Iqra University High Performance Research Group Karachi Pakistan

ISBN: (纸本)9798350335316

COVID-19 is highly infectious and has been extensively spread worldwide, with approximately 651 million definite cases crosswise the globe including Pakistan. At that era of pandemic where patients are not able to approach a doctor for even the routine checkups, in such curial situation even normal disease checkups are ignored by many families due to pandemic situations, those diseases may lead to be a perilous disease are results of it. Human disorders portray scenarios that even disturb or permanently cutoff the essential functions of a body parts. Consequently, the aim is to transform raw health data potential into actionable insights to applying the promising outcomes of Body Sensor Network (BSN) and State-of-Art Artificial Intelligence (AI) techniques to get proper medicine allocation to the particular health state of patient. In this paper the different techniques of Deep Learning and Machine Learning introduced to predict the actual medicine for the specific health state of patient according to data from the BSN. Experiments have been conducted on large dataset which shepherd it into 16 states of patient's health which will allotted to AI model to predict the medicine accordingly to the health state of patient. Experimental results show the 87.46% by Random Forest, 92.74% by K-Nearest Neighbors, 74.57% by Naive Bayes, 94.41% by Extreme Gradient Boost, 84.88% by Multi-Layer Perceptron in terms of precision of model training in event of classification. © 2023 IEEE.

关键词： Nearest neighbor search

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Evaluating Energy Efficiency of GPUs using Machine Learning Benchmarks

Evaluating Energy Efficiency of GPUs using Machine Learning ...

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IEEE International Symposium on Parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Brett Foster Shubbhi Taneja Joseph Manzano Kevin Barker Department of Computer Science Worcester Polytechnic Institute High Performance Computing Group Pacific Northwest National Laboratory

As we enter the exascale era, the energy efficiency and performance of high-performance computing (HPC) systems, especially running Machine Learning (ML) applications, are becoming increasingly important. Nvidia recently released its 9th-generation HPC-grade Graphics Processing Unit (GPU) microarchitecture, Ampere, claiming significant improvements over the previous generation’s Volta architecture. In this paper, we perform fine-grained power collection and assess the performance of these two HPC architectures’ performance by profiling ML benchmarks. In addition, we analyze various hyperparameters, primarily the batch size and the number of GPUs, to determine their impact on these systems’ performance and power efficiency. While Ampere is 3.16x more energy-efficient than Volta in isolation, this is counteracted by the PCIe interconnects of the A100s as the ML tasks are parallelized to run on more GPUs.

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Editorial

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Journal of Reliable Intelligent Environments 2021年第4期7卷 277-278页

作者： Augusto, Juan Carlos Coronato, Antonio Research Group on Development of Intelligent Environments Department of Computer Science Middlesex University London United Kingdom Institute for High Performance Computing and Networking National Research Council Naples Italy

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Transformative effects of ChatGPT on modern education: Emerging Era of AI Chatbots

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Internet of Things and Cyber-Physical Systems 2024年第1期4卷 19-23页

作者： Gill, Sukhpal Singh Xu, Minxian Patros, Panos Wu, Huaming Kaur, Rupinder Kaur, Kamalpreet Fuller, Stephanie Singh, Manmeet Arora, Priyansh Parlikad, Ajith Kumar Stankovski, Vlado Abraham, Ajith Ghosh, Soumya K. Lutfiyya, Hanan Kanhere, Salil S. Bahsoon, Rami Rana, Omer Dustdar, Schahram Sakellariou, Rizos Uhlig, Steve Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Raygun Performance Monitoring Wellington New Zealand Center for Applied Mathematics Tianjin University Tianjin China Department of Science Kings Education London United Kingdom Cymax Group Technologies BC Canada QM Academy Queen Mary University of London London United Kingdom Jackson School of Geosciences University of Texas at Austin AustinTX United States Centre for Climate Change Research Indian Institute of Tropical Meteorology Pune India Microsoft Hyderabad India Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Machine Intelligence Research Labs AuburnWA United States Faculty of Computing and Data Science FLAME University Maharashtra Pune India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science University of Western Ontario London Canada Sydney Australia School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

ChatGPT, an AI-based chatbot, offers coherent and useful replies based on analysis of large volumes of data. In this article, leading academics, scientists, distinguish researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research discusses ChatGPT capabilities and its use in the education sector, identifies potential concerns and challenges. Our preliminary evaluation shows that ChatGPT perform differently in different subject areas including finance, coding, maths, and general public queries. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions, transforming education through smartphones and IoT gadgets, and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported "hallucinations" within GenerativeAI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial. © 2023 The Authors

关键词： Machine learning

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Editorial

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Journal of Reliable Intelligent Environments 2021年第2期7卷 67-67页

作者： Coronato, Antonio Augusto, Juan Carlos Department of Computer Science Research Group on Development of Intelligent Environments Middlesex University London United Kingdom Institute for High Performance Computing and Networking National Research Council Naples Italy

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SmartShip: Data Decoding Optimization for Onboard AI Anomaly Detection

SmartShip: Data Decoding Optimization for Onboard AI Anomaly...

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International Symposium on Parallel and distributed computing

作者： Pavle Ivanovic Alexander Windmann Philipp Neumann Chair for High Performance Computing Helmut Schmidt University Hamburg Germany Chair for Computer Science in Mechanical Engineering Helmut Schmidt University Hamburg Germany Deutsches Elektronen-Synchroton Research Group High Performance Computing and Data Science Universität Hamburg Hamburg Germany

ISBN: (数字)9798350369199

ISBN: (纸本)9798350369205

Maritime rescue operations rely on efficient and resilient sea vessels for responding to offshore emergencies. To ensure the availability and prompt deployment of rescue cruisers, system operators need to recognize (and resolve) the failure patterns before they occur onboard. However, many SAR organizations do not have sufficient computing resources for advanced anomaly detection and future fault prediction. This paper details our performant and cost-effective solution for sensor data acquisition, CAN bus decoding, and AI analysis, deployable on typical maritime edge devices. The main focus of our work is enhancing the decoding speed, which is a prerequisite for efficient AI analyses and anomaly detection. Besides elaboration on different optimization strategies, we provide the actual use case for AI anomaly detection based on anomaly scores of electrical, fuel, and cooling system sensors. As a result, we improved the decoding speed by more than two orders of magnitude, allowing edge outlier detection in minutes rather than days.

关键词： performance evaluation System testing Image edge detection Data acquisition Decoding Pattern recognition Artificial intelligence Anomaly detection Optimization Tuning

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A First-Order Multi-Gradient Algorithm for Multi-Objective Bi-Level Optimization 27

A First-Order Multi-Gradient Algorithm for Multi-Objective B...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Ye, Feiyang Lin, Baijiong Cao, Xiaofeng Zhang, Yu Tsang, Ivor W. Department of Computer Science and Engineering Southern University of Science and Technology China Australian Artificial Intelligence Institute University of Technology Sydney Australia The Hong Kong University of Science and Technology Guangzhou China School of Artificial Intelligence Jilin University China Shanghai Artificial Intelligence Laboratory China Centre for Frontier AI Research Agency for Science Technology and Research Singapore Institute of High Performance Computing Agency for Science Technology and Research Singapore School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (纸本)9781643685489

In this paper, we study the Multi-Objective Bi-Level Optimization (MOBLO) problem, where the upper-level subproblem is a multi-objective optimization problem and the lower-level subproblem is for scalar optimization. Existing gradient-based MOBLO algorithms need to compute the Hessian matrix, causing the computational inefficient problem. To address this, we propose an efficient first-order multi-gradient method for MOBLO, called FORUM. Specifically, we reformulate MOBLO problems as a constrained multi-objective optimization (MOO) problem via the value-function approach. Then we propose a novel multi-gradient aggregation method to solve the challenging constrained MOO problem. Theoretically, we provide the complexity analysis to show the efficiency of the proposed method and a non-asymptotic convergence result. Empirically, extensive experiments demonstrate the effectiveness and efficiency of the proposed FORUM method in different learning problems. In particular, it achieves state-of-the-art performance on three multi-task learning benchmark datasets. © 2024 The Authors.

关键词： Optimization algorithms

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