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Proceedings of mlhpc 2021: workshop on machine learning in high performance computing environments, Held in conjunction with SC 2021: The International Conference for high performance computing, Networking, Storage and Analysis

Proceedings of MLHPC 2021: Workshop on Machine Learning in H...

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7th IEEE/ACM workshop on machine learning in high performance computing environments, mlhpc 2021

ISBN: (纸本)9781665411240

The proceedings contain 9 papers. The topics discussed include: semantic-aware lossless data compression for deep learning recommendation model (DLRM);Colmena: scalable machine-learning-based steering of ensemble simulations for high performance computing;production deployment of machine-learned rotorcraft surrogate models on HPC;high-performance deep learning toolbox for genome-scale prediction of protein structure and function;HPCFAIR: enabling FAIR AI for HPC applications;HPC ontology: towards a unified ontology for managing training datasets and AI models for high-performance computing;HYPPO: a surrogate-based multi-level parallelism tool for hyperparameter optimization;and is disaggregation possible for hpc cognitive simulation?.

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Proceedings of 2020 IEEE/ACM workshop on machine learning in high performance computing environments, mlhpc 2020 and workshop on Artificial Intelligence and machine learning for Scientific Applications, AI4S 2020 - Held in conjunction with SC 2020: The International Conference for high performance computing, Networking, Storage and Analysis

Proceedings of 2020 IEEE/ACM Workshop on Machine Learning in...

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6th IEEE/ACM workshop on machine learning in high performance computing environments, mlhpc 2020 and 1st workshop on Artificial Intelligence and machine learning for Scientific Applications, AI4S 2020

ISBN: (纸本)9780738110783

The proceedings contain 11 papers. The topics discussed include: accelerate distributed stochastic descent for Nonconvex optimization with momentum;accelerating GPU-based machine learning in python using MPI library: a case study with MVAPICH2-GDR;deep learning-based low-dose tomography reconstruction with hybrid-dose measurements;EventGraD: event-triggered communication in parallel stochastic gradient descent;a benders decomposition approach to correlation clustering;high-bypass learning: automated detection of tumor cells that significantly impact drug response;deep generative models that solve PDEs: distributed computing for training large data-free models;automatic particle trajectory classification in plasma simulations;reinforcement learning-based solution to power grid planning and operation under uncertainties;and predictions of steady and unsteady flows using machine-learned surrogate models.

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Proceedings of mlhpc 2019: 5th workshop on machine learning in HPC environments - Held in conjunction with SC 2019: The International Conference for high performance computing, Networking, Storage and Analysis

Proceedings of MLHPC 2019: 5th Workshop on Machine Learning ...

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5th IEEE/ACM workshop on machine learning in HPC environments, mlhpc 2019

ISBN: (纸本)9781728159850

The proceedings contain 8 papers. The topics discussed include: scalable hyperparameter optimization with lazy Gaussian processes;understanding scalability and fine-grain parallelism of synchronous data parallel training;DisCo: physics-based unsupervised discovery of coherent structures in spatiotemporal systems;GradVis: visualization and second order analysis of optimization surfaces during the training of deep neural networks;metaoptimization on a distributed system for deep reinforcement learning;scheduling optimization of parallel linear algebra algorithms using supervised learning;parallel data-local training for optimizing Word2Vec embeddings for word and graph embeddings;and fine-grained exploitation of mixed precision for faster CNN training.

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Proceedings of mlhpc 2015: machine learning in high-performance computing environments - Held in conjunction with SC 2015: The International Conference for high performance computing, Networking, Storage and Analysis

Proceedings of MLHPC 2015: Machine Learning in High-Performa...

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workshop on machine learning in high-performance computing environments, mlhpc 2015

ISBN: (纸本)9781450340069

The proceedings contain 5 papers. The topics discussed include: asynchronous parallel stochastic gradient descent - a numeric core for scalable distributed machine learning algorithms;HPDBSCAN - highly parallel DBSCAN;dynamic adaptive neural network arrays: a neuromorphic architecture;optimizing deep learning hyper-parameters through an evolutionary algorithm;and LBANN: Livermore big artificial neural network HPC toolkit.

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MLG-HPCE 2024: International workshop on machine learning with Graphs in high performance computing environments

MLG-HPCE 2024: International Workshop on Machine Learning wi...

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high performance computing, Networking, Storage and Analysis, SC-W: workshops of the International Conference for

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Comparative Study of Embedded Energy Management Methods based on machine learning for Dual-Source Electric Vehicles

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IEEE Transactions on Transportation Electrification 2025年

作者： Adnane, Marouane Nguyen, Bao-Huy Khoumsi, Ahmed Trovao, Joao Pedro F. Université de Sherbrooke Department of Electrical Engineering and Computer Engineering SherbrookeQCJ1K 2R1 Canada Hanoi University of Science and Technology CTI Lab4EV School of Electrical and Electronic Engineering Hanoi Viet Nam

In the actual context of dual-source electric vehicles (DSEVs), efficient energy management strategies (EMSs) are essential to optimize energy distribution between batteries and supercapacitors. However, achieving real-time decision-making and adaptability in resource-constrained environments remains a significant challenge. This study addresses these challenges by comparing two advanced machine learning (ML)-based EMSs: Driving Mode Prediction-based EMS (DMP-EMS) and Multi-Ensemble learning-based EMS (MEL-EMS). Using Signal Hardware-In-the-Loop simulation and a Raspberry Pi 4 platform, the two EMSs are evaluated based on various criteria, including performance accuracy, model complexity, training and prediction times, required computing resources, scalability, and energy consumption. The findings highlight the trade-offs between these EMSs, offering insights into designing tailored EMSs for DSEVs under varying operational demands. While MEL-EMS excels in environments where prediction accuracy is paramount since it offers superior predictive performance suitable for applications demanding high accuracy and scalability, DMP-EMS provides a more efficient and practical solution for real-time energy management in resource-constrained settings. © 2015 IEEE.

关键词： Hardware-in-the-loop simulation

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MLG-HPCE 2024: International workshop on machine learning with Graphs in high performance computing environments

Proceedings of SC 2024-W: Workshops of the International Con...

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Proceedings of SC 2024-W: workshops of the International Conference for high performance computing, Networking, Storage and Analysis 2024年 1035-1036页

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AI-Driven Personalization in Organizational Communication

AI-Driven Personalization in Organizational Communication

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IEEE Communication Strategies in Digital Society workshop (ComSDS)

作者： Denis Shakhov Ruslan Batashev Ilyos Abdullayev Department of Economics and Management Khorezm University of Economics Urgench Uzbekistan Department of Taxes and Taxation Kadyrov Chechen State University Grozny Russia Department of Business and Management Urgench State University Urgench Uzbekistan

ISBN: (数字)9798331536121

ISBN: (纸本)9798331536138

This study investigates the transformative impact of artificial intelligence on corporate communications, focusing on AI-powered personalization systems in business environments. Through a systematic literature review (2019-2024), the research establishes an empirical framework for evaluating these systems’ technological infrastructure. The findings reveal distinct sector-specific performance variations: the retail sector showing 58% enhanced engagement metrics, while B2B segments demonstrated 28% improvement in key performance indicators. The technological foundation comprises machine learning algorithms, natural language processing frameworks, and high-performance computing systems enabling real-time personalization. The methodology integrates the adaptive personalization framework (APF) with the multidimensional personalization model (MPM) to elucidate machine learning mechanisms. This framework supports user profiling, navigation optimization, and behavioral pattern modification, secured through distributed ledger technologies. Empirical analysis reveals the complementarity between AI and human capabilities. While AI systems excel in response velocity (mean: 4.92), human interactions demonstrate superior responsiveness (5.27) and professional competency metrics (5.32 vs. 4.87), suggesting the optimality of a hybrid model. The study culminates in a conceptual framework balancing communication scalability with personalized relevance while adhering to ethical imperatives of data protection, algorithmic fairness, and transparency protocol.

关键词： Seminars Ethics Scalability Industrial communication machine learning Natural language processing Real-time systems Optimization Standards Systematic literature review

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End-to-End Requirements Mapping for Cloud-Native Applications Using ML Models

End-to-End Requirements Mapping for Cloud-Native Application...

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IEEE Annual computing and Communication workshop and Conference (CCWC)

作者： Mbarka Soualhia Fetahi Wuhib Ericsson Research Montreal QC Canada

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

In the rapidly evolving landscape of 5G and emerging 6G networks, ensuring cloud-based applications meet stringent performance and reliability requirements is critical to maintaining a competitive edge and satisfying user demands. These applications are typically composed of interconnected components, such as Virtual Network Functions (VNFs) or microservices, each contributing to the application's overall performance. However, configuring individual VNFs or microservices to meet specific end-to-end requirements—such as latency and throughput—poses a significant challenge. Current methods often rely on manual configurations, which can lead to inefficiencies and suboptimal resource use in dynamic cloud environments. In this paper, we introduce a machine learning-based solution to automate the mapping of end-to-end requirements into individual KPI requirements for each component (e.g., VNF) in the service graph. Using Deep Neural Networks (DNN) and Graph Neural Networks (GNN), our solution learns from diverse deployment scenarios and end-to-end application requirements, enabling accurate prediction of the KPIs needed for each component to support overall performance goals. This approach ensures that all components are precisely configured to meet end-to-end requirements without extensive manual tuning. Our evaluation on two distinct applications demonstrates high accuracy in predictions, achieving up to 98.99% accuracy during training and up to 96.67% in inferencing, surpassing the accuracy of expert-configured setups.

关键词： Training Cloud computing Accuracy Microservice architectures Manuals Artificial neural networks Dynamic scheduling Graph neural networks Reliability Tuning

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high-performance Deep learning Toolbox for Genome-Scale Prediction of Protein Structure and Function 7

High-Performance Deep Learning Toolbox for Genome-Scale Pred...

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7th IEEE/ACM workshop on machine learning in high performance computing environments (mlhpc)

作者： Gao, Mu Lund-Andersen, Peik Morehead, Alex Mahmud, Sajid Chen, Chen Chen, Xiao Giri, Nabin Roy, Raj S. Quadir, Farhan Effler, T. Chad Prout, Ryan Abraham, Subil Elwasif, Wael Haas, N. Quentin Skolnick, Jeffrey Cheng, Jianlin Sedova, Ada Georgia Inst Technol Atlanta GA 30332 USA Univ Idaho Moscow ID 83843 USA Oak Ridge Natl Lab Oak Ridge TN 37830 USA Univ Missouri Columbia MO 65211 USA

ISBN: (纸本)9781665411240

Computational biology is one of many scientific disciplines ripe for innovation and acceleration with the advent of high-performance computing (HPC). In recent years, the field of machine learning has also seen significant benefits from adopting HPC practices. In this work, we present a novel HPC pipeline that incorporates various machine-learning approaches for structure-based functional annotation of proteins on the scale of whole genomes. Our pipeline makes extensive use of deep learning and provides computational insights into best practices for training advanced deep-learning models for high-throughput data such as proteomics data. We showcase methodologies our pipeline currently supports and detail future tasks for our pipeline to envelop, including large-scale sequence comparison using SAdLSA and prediction of protein tertiary structures using AlphaFold2.

关键词： high-performance computing computational biology machine learning deep learning protein sequence alignment protein structure prediction

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