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MAPA: Multi-accelerator pattern allocation policy for multi-tenant GPU servers

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arXiv 2021年

作者： Ranganath, Kiran Suetterlein, Joshua D. Manzano, Joseph B. Song, Shuaiwen Leon Wong, Daniel Department of Electrical and Computer Engineering University of California RiversideCA United States High-Performance Computing Group Pacific Northwest National Lab WA United States Future System Architecture Lab School of Computer Science University of Sydney SydneyNSW Australia

Multi-accelerator servers are increasingly being deployed in shared multi-tenant environments (such as in cloud data centers) in order to meet the demands of large-scale compute-intensive workloads. In addition, these accelerators are increasingly being inter-connected in complex topologies and workloads are exhibiting a wider variety of inter-accelerator communication patterns. However, existing allocation policies are ill-suited for these emerging use-cases. Specifically, this work identifies that multi-accelerator workloads are commonly fragmented leading to reduced bandwidth and increased latency for inter-accelerator communication. We propose Multi-Accelerator Pattern Allocation (MAPA), a graph pattern mining approach towards providing generalized allocation support for allocating multi-accelerator workloads on multi-accelerator servers. We demonstrate that MAPA is able to improve the execution time of multi-accelerator workloads and that MAPA is able to provide generalized benefits across various accelerator topologies. Finally, we demonstrate a speedup of 12.4% for 75th percentile of jobs with the worst case execution time reduced by up to 35% against baseline policy using MAPA. © 2021, CC BY-NC-SA.

关键词： Graphics processing unit

Quantum Uncertainty Principles for Measurements with Interventions

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Physical Review Letters 2023年第24期130卷 240201-240201页

作者： Yunlong Xiao Yuxiang Yang Ximing Wang Qing Liu Mile Gu Institute of High Performance Computing (IHPC) Agency for Science Technology and Research (A*STAR) 1 Fusionopolis Way No. 16-16 Connexis Singapore 138632 Republic of Singapore Nanyang Quantum Hub School of Physical and Mathematical Sciences Nanyang Technological University Singapore 637371 Singapore QICI Quantum Information and Computation Initiative Department of Computer Science The University of Hong Kong Pokfulam Road Hong Kong Institute for Theoretical Physics ETH Zürich 8093 Zürich Switzerland Key Laboratory for Information Science of Electromagnetic Waves (Ministry of Education) Fudan University Shanghai 200433 China Centre for Quantum Technologies National University of Singapore Singapore 117543 Singapore MajuLab CNRS-UNS-NUS-NTU International Joint Research Unit UMI 3654 Singapore 117543 Singapore

Heisenberg’s uncertainty principle implies fundamental constraints on what properties of a quantum system we can simultaneously learn. However, it typically assumes that we probe these properties via measurements at a single point in time. In contrast, inferring causal dependencies in complex processes often requires interactive experimentation—multiple rounds of interventions where we adaptively probe the process with different inputs to observe how they affect outputs. Here, we demonstrate universal uncertainty principles for general interactive measurements involving arbitrary rounds of interventions. As a case study, we show that they imply an uncertainty trade-off between measurements compatible with different causal dependencies.

关键词： Quantum foundations Quantum information theory Quantum measurements Quantum memories Quantum networks

Efficient Pyramid Channel Attention Network for Pathological Myopia Recognition

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arXiv 2023年

作者： Zhang, Xiaoqing Zhao, Jilu Li, Yan Wu, Hao Zhou, Xiangtian Liu, Jiang Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Center for High Performance Computing Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China National Clinical Research Center for Ocular Diseases Eye Hospital Wenzhou Medical University Wenzhou325027 China State Key Laboratory of Ophthalmology Optometry and Visual Science Eye Hospital Wenzhou Medical University Wenzhou325027 China Research Unit of Myopia Basic Research and Clinical Prevention and Control Chinese Academy of Medical Sciences Wenzhou325027 China Singapore Eye Research Institute 169856 Singapore

Pathological myopia (PM) is the leading ocular disease for impaired vision worldwide. Clinically, the characteristic of pathology distribution in PM is global-local on the fundus image, which plays a significant role in assisting clinicians in diagnosing PM. However, most existing deep neural networks focused on designing complex architectures but rarely explored the pathology distribution prior of PM. To tackle this issue, we propose an efficient pyramid channel attention (EPCA) module, which fully leverages the potential of the clinical pathology prior of PM with pyramid pooling and multi-scale context fusion. Then, we construct EPCA-Net for automatic PM recognition based on fundus images by stacking a sequence of EPCA modules. Moreover, motivated by the recent pretraining-and-finetuning paradigm, we attempt to adapt pre-trained natural image models for PM recognition by freezing them and treating the EPCA and other attention modules as adapters. In addition, we construct a PM recognition benchmark termed PM-fundus by collecting fundus images of PM from publicly available datasets. The comprehensive experiments demonstrate the superiority of EPCA-Net over state-of-the-art methods in the PM recognition task. For example, EPCA-Net achieves 97.56% accuracy and outperforms ViT by 2.85% accuracy on the PM-fundus dataset. The results also show that our method based on the pretraining-and-finetuning paradigm achieves competitive performance through comparisons to part of previous methods based on traditional fine-tuning paradigm with fewer tunable parameters, which has the potential to leverage more natural image foundation models to address the PM recognition task in limited medical data regime. Copyright © 2023, The Authors. All rights reserved.

关键词： Pathology

Transformative Effects of ChatGPT on Modern Education: Emerging Era of AI Chatbots

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arXiv 2023年

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 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, was released to provide coherent and useful replies based on analysis of large volumes of data. In this article, leading scientists, researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research seeks to improve our knowledge of ChatGPT capabilities and its use in the education sector, identifying potential concerns and challenges. Our preliminary evaluation concludes that ChatGPT performed differently in each subject area including finance, coding and maths. 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 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 Generative AI 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, CC0.

关键词： Machine learning

Modeling Neural Networks Training Process with Markov Decision Process

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Modeling Neural Networks Training Process with Markov Decisi...

Artificial Intelligence and computer engineering (ICAICE), International Conference on

作者： Yantao Bai Wanwei Liu Xinjun Mao Zhen Liang Key Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha China College of Computer Science National University of Defense Technology Changsha China State Key Laboratory of High Performance Computing Institute for Quantum Information &#x0026 National University of Defense Technology Changsha China

ISBN: (纸本)9781665421874

With the development of computer technology, statistics-based machine learning method has made great break-throughs, and also improved the development of artificial intelligence. Nevertheless, as a very influential model, neural networks are still treated as “black boxes”. The results of neural networks are extremely sensitive to the training samples, which lead to great challenges to the controllability of the algorithm. With the wide application of machine learning, demand for interpretability and controllability of neural networks algorithms is increasing. As a result, various scholars have tried to explain and verify neural networks algorithms based on formal methods in recent years. In this paper, a method (called MNNTP) is presented to model the training process of neural networks by using a Markov decision process (MDP). Through MNNTP, the neural networks are abstracted into the form of MDP, which makes notable contributions for verifying some mathematical properties of the neural networks.

关键词： Training Machine learning algorithms Computational modeling Neural networks Machine learning Markov processes Controllability

Application of an ontology based process model construction tool for active protective coatings: Corrosion inhibitor release

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Application of an ontology based process model construction ...

Joint Ontology Workshops 2021 Episode VII: The Bolzano Summer of Knowledge, JOWO 2021

作者： Klein, Peter Preisig, Heinz A. Horsch, Martin Thomas Konchakova, Natalia Fraunhofer Institute for Industrial Mathematics Fraunhofer-Platz 1 Kaiserslautern67663 Germany Department of Chemical Engineering Norwegian University of Science and Technology Trondheim Norway High Performance Computing Center Stuttgart Nobelstr. 19 Stuttgart70569 Germany Stfc Daresbury Laboratory Uk Research and Innovation Keckwick Ln DaresburyWA4 4AD United Kingdom Institute of Surface Science Helmholtz-Zentrum Hereon Max-Planck-Straße 1 Geesthacht21502 Germany

Ontology-based integrated materials modelling for an active protective coating system design is presented and applied to a practical example. For this purpose, an ontological methodology implemented using the ProMo (Process Modelling) suite is developed to be used with an open simulation platform (OSP), i.e., a workflow management and orchestration framework that can be integrated into digital infrastructures. The target infrastructures, which are under development in various Horizon 2020 projects, include modelling marketplaces, open innovation platforms, and open translation environments among others. Semantic interoperability for the communication between the involved digital infrastructures, including the simulation hubs, relies on the Review of Materials Modelling (RoMM), MODA (Modelling Data), and the Ontology for Simulation, Modelling, and Optimization (OSMO) in combination with the Physicalistic Interpretation of Modelling and Simulation Interoperability Infrastructure (PIMS-II) midlevel ontology, which is aligned with the Elementary Multiperspective Material Ontology (EMMO) as a top-level ontology. The challenge of addressing semantic heterogeneity is addressed by working toward crosswalks between domain-specific and mid-level ontologies for industrially relevant problems, where knowledge graph transformation is evaluated as a candidate solution for a future implementation strategy. The involved semantic artefacts are platform-agnostic, and their EMMO compliance allows for a specification of executable modelling and simulation workflows on multiple EMMO-compliant OSPs. We demonstrate the presented approach on industrial relevant example for development of active corrosion protection of metallic surfaces. © 2021 CEUR-WS. All rights reserved.

关键词： Ontology

Reducing high-Risk Glucose Forecasting Errors by Evolving Interpretable Models for Type 1 Diabetes

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SSRN

SSRN 2022年

作者： Cioppa, Antonio Della De Falco, Ivanoe Koutny, Tomas Scafuri, Umberto Ubl, Martin Tarantino, Ernesto NCLab DIEM University of Salerno SA Fisciano84084 Italy Institute of High Performance Computing and Networking National Research Council of Italy Naples80131 Italy DCSE Department of Computer Science Engineering University of West Bohemia Pilsen306 14 Czech Republic NTIS New Technologies for the Information Society University of West Bohemia Pilsen306 14 Czech Republic

Diabetes mellitus is a metabolic disease involving high blood glucose levels that can lead to serious medical consequences. Hence, for diabetic patients the prediction of future glucose levels is essential in the management of the *** of the forecasting approaches in the literature evaluate the effectiveness of glucose predictors only with numerical *** approaches are limited because they evenly treat all the errors without considering their different clinical impact that could involve lethal effects in dangerous situations such as hypo- or hyperglycemia. To overcome such a limitation, this paper aims to devise models for reducing high-risk glucose forecasting errors for Type 1 diabetic *** this purpose, we exploit a Grammatical Evolution algorithm to induce personalized and interpretable forecasting glucose models assessed with a novel, composite metric to satisfy both clinical and numerical requirements of the estimated *** assess the effectiveness of the proposed approach, a real-world data set widely used in literature, consisting of data from several patients suffering from Type 1 diabetes, has been adopted. The experimental findings show that the induced models are interpretable and capable of assuring predictions with a good tradeoff between medical quality and numerical accuracy. Furthermore, their performance is better than or comparable to that of other state–of–the–art methods. © 2022, The Authors. All rights reserved.

关键词： Forecasting

Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model

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arXiv 2021年

作者： Wang, Qizhou Han, Bo Liu, Tongliang Niu, Gang Yang, Jian Gong, Chen Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of MoE School of Computer Science and Engineering Nanjing University of Science and Technology China Department of Computer Science Hong Kong Baptist University Hong Kong Trustworthy Machine Learning Lab School of Computer Science Faculty of Engineering The University of Sydney Australia Japan Department of Computing Hong Kong Polytechnic University Hong Kong

The drastic increase of data quantity often brings the severe decrease of data quality, such as incorrect label annotations, which poses a great challenge for robustly training Deep Neural Networks (DNNs). Existing learning methods with label noise either employ ad-hoc heuristics or restrict to specific noise assumptions. However, more general situations, such as instance-dependent label noise, have not been fully explored, as scarce studies focus on their label corruption process. By categorizing instances into confusing and unconfusing instances, this paper proposes a simple yet universal probabilistic model, which explicitly relates noisy labels to their instances. The resultant model can be realized by DNNs, where the training procedure is accomplished by employing an alternating optimization algorithm. Experiments on datasets with both synthetic and real-world label noise verify that the proposed method yields significant improvements on robustness over state-of-the-art counterparts. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep neural networks

Tuning Dzyaloshinskii-Moriya interaction in ferrimagnetic GdCo: A first-principles approach

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Physical Review B 2021年第17期103卷 174414-174414页

作者： Md Golam Morshed Khoong Hong Khoo Yassine Quessab Jun-Wen Xu Robert Laskowski Prasanna V. Balachandran Andrew D. Kent Avik W. Ghosh Department of Electrical and Computer Engineering University of Virginia Charlottesville Virginia 22904 USA Institute of High Performance Computing Agency for Science Technology and Research 1 Fusionopolis Way Connexis Singapore 138632 Singapore Center for Quantum Phenomena Department of Physics New York University New York New York 10003 USA Department of Materials Science and Engineering University of Virginia Charlottesville Virginia 22904 USA Department of Mechanical and Aerospace Engineering University of Virginia Charlottesville Virginia 22904 USA Department of Physics University of Virginia Charlottesville Virginia 22904 USA

We present a systematic analysis of our ability to tune chiral Dzyaloshinskii-Moriya interaction (DMI) in compensated ferrimagnetic Pt/GdCo/Pt1−xWx trilayers by cap layer composition. Using first-principles calculations, we show that the DMI increases rapidly for only ∼10% W and saturates thereafter, in agreement with experiments. The calculated DMI shows a spread in values around the experimental mean, depending on the atomic configuration of the cap layer interface. The saturation is attributed to the vanishing of spin-orbit coupling energy at the cap layer and the simultaneous constancy at the bottom interface. Additionally, we predict the DMI in Pt/GdCo/X (X=Ta,W,Ir) and find that W in the cap layer favors a higher DMI than Ta and Ir that can be attributed to the difference in d-band overlap around the Fermi level. Our results open up exciting combinatorial possibilities for controlling the DMI in ferrimagnets towards nucleating and manipulating ultrasmall high-speed skyrmions.

关键词： Dzyaloshinskii-Moriya interaction Ferrimagnetism Magnetic multilayers First-principles calculations