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Found 18 papers, 6 papers with code
Flight: A FaaS-Based Framework for Complex and Hierarchical Federated Learning
Federated Learning (FL) is a decentralized machine learning paradigm where models are trained on distributed devices and are aggregated at a central server.
Employing Artificial Intelligence to Steer Exascale Workflows with Colmena
Computational workflows are a common class of application on supercomputers, yet the loosely coupled and heterogeneous nature of workflows often fails to take full advantage of their capabilities.
Trillion Parameter AI Serving Infrastructure for Scientific Discovery: A Survey and Vision
Deep learning methods are transforming research, enabling new techniques, and ultimately leading to new discoveries.
Linking the Dynamic PicoProbe Analytical Electron-Optical Beam Line / Microscope to Supercomputers
The Dynamic PicoProbe at Argonne National Laboratory is undergoing upgrades that will enable it to produce up to 100s of GB of data per day.
APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a Service
Cross-silo privacy-preserving federated learning (PPFL) is a powerful tool to collaboratively train robust and generalized machine learning (ML) models without sharing sensitive (e. g., healthcare of financial) local data.
AI-assisted Automated Workflow for Real-time X-ray Ptychography Data Analysis via Federated Resources
We present an end-to-end automated workflow that uses large-scale remote compute resources and an embedded GPU platform at the edge to enable AI/ML-accelerated real-time analysis of data collected for x-ray ptychography.
Cloud Services Enable Efficient AI-Guided Simulation Workflows across Heterogeneous Resources
Applications that fuse machine learning and simulation can benefit from the use of multiple computing resources, with, for example, simulation codes running on highly parallel supercomputers and AI training and inference tasks on specialized accelerators.
OpenHLS: High-Level Synthesis for Low-Latency Deep Neural Networks for Experimental Science
In many experiment-driven scientific domains, such as high-energy physics, material science, and cosmology, high data rate experiments impose hard constraints on data acquisition systems: collected data must either be indiscriminately stored for post-processing and analysis, thereby necessitating large storage capacity, or accurately filtered in real-time, thereby necessitating low-latency processing.
Deep learning at the edge enables real-time streaming ptychographic imaging
Coherent microscopy techniques provide an unparalleled multi-scale view of materials across scientific and technological fields, from structural materials to quantum devices, from integrated circuits to biological cells.
Globus Automation Services: Research process automation across the space-time continuum
We report here on new services within the Globus research data management platform that enable the specification of diverse research processes as reusable sets of actions, \emph{flows}, and the execution of such flows in heterogeneous research environments.
FAIR principles for AI models with a practical application for accelerated high energy diffraction microscopy
A concise and measurable set of FAIR (Findable, Accessible, Interoperable and Reusable) principles for scientific data is transforming the state-of-practice for data management and stewardship, supporting and enabling discovery and innovation.
Colmena: Scalable Machine-Learning-Based Steering of Ensemble Simulations for High Performance Computing
Scientific applications that involve simulation ensembles can be accelerated greatly by using experiment design methods to select the best simulations to perform.
Ultrafast Focus Detection for Automated Microscopy
Technological advancements in modern scientific instruments, such as scanning electron microscopes (SEMs), have significantly increased data acquisition rates and image resolutions enabling new questions to be explored; however, the resulting data volumes and velocities, combined with automated experiments, are quickly overwhelming scientists as there remain crucial steps that require human intervention, for example reviewing image focus.
Accelerated, Scalable and Reproducible AI-driven Gravitational Wave Detection
The development of reusable artificial intelligence (AI) models for wider use and rigorous validation by the community promises to unlock new opportunities in multi-messenger astrophysics.
Towards Online Steering of Flame Spray Pyrolysis Nanoparticle Synthesis
Flame Spray Pyrolysis (FSP) is a manufacturing technique to mass produce engineered nanoparticles for applications in catalysis, energy materials, composites, and more.
Targeting SARS-CoV-2 with AI- and HPC-enabled Lead Generation: A First Data Release
Researchers across the globe are seeking to rapidly repurpose existing drugs or discover new drugs to counter the the novel coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).
funcX: A Federated Function Serving Fabric for Science
These new approaches must enable computation to be mobile, so that, for example, it can occur near data, be triggered by events (e. g., arrival of new data), be offloaded to specialized accelerators, or run remotely where resources are available.
Distributed, Parallel, and Cluster Computing
DLHub: Model and Data Serving for Science
Here we present the Data and Learning Hub for science (DLHub), a multi-tenant system that provides both model repository and serving capabilities with a focus on science applications.
