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 simu...
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 highperformancecomputing;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-performancecomputing;HYPPO: a surrogate-based multi-level parallelism tool for hyperparameter optimization;and is disaggregation possible for hpc cognitive simulation?.
The proceedings contain 11 papers. The topics discussed include: accelerate distributed stochastic descent for Nonconvex optimization with momentum;accelerating GPU-based machinelearning in python using MPI library: ...
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 machinelearning 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.
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 train...
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.
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 rea...
详细信息
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...
详细信息
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 machinelearning algorithms, natural language processing frameworks, and high-performancecomputing systems enabling real-time personalization. The methodology integrates the adaptive personalization framework (APF) with the multidimensional personalization model (MPM) to elucidate machinelearning 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.
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 ...
详细信息
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 machinelearning-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.
Computational biology is one of many scientific disciplines ripe for innovation and acceleration with the advent of high-performancecomputing (HPC). In recent years, the field of machinelearning has also seen signif...
详细信息
ISBN:
(纸本)9781665411240
Computational biology is one of many scientific disciplines ripe for innovation and acceleration with the advent of high-performancecomputing (HPC). In recent years, the field of machinelearning 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.
暂无评论