Bridging Data Center AI Systems with Edge Computing for Actionable Information Retrieval

Liu, Zhengchun; Ali, Ahsan; Kenesei, Péter; Miceli, Antonino; Sharma, Hemant; Schwarz, Nina; Trujillo, Dennis; Yoo, Hyunseung; Coffee, Ryan; Layad, Naoufal; Thayer, J. B.; Herbst, R.; Yoon, Chunhong; Foster, Ian

doi:10.1109/xloop54565.2021.00008

Cited by 21 publications

(13 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this increasingly popular approach to data reduction, previously collected data (from current or prior experiments and/or simulations) are used to train ML models to recognize interesting phenomena for data reduction or rapid response. 26 , 27 , 28 , 29 , 30 …”

Section: Resultsmentioning

confidence: 99%

“…The BraggNN flow, as shown in Figure 3 , explores the feasibility of this approach and, in particular, the relative costs of data transfer, network training, and network deployment. It comprises just four steps 28 , 78 : (1) copy data from beamline to computing facility (transfer); (2) prepare the data for training (compute); (3) train the BraggNN model (compute); and (4) copy the trained model back to the beamline (transfer). In the experiments described below, data are collected at SSRL and transferred to ALCF for training on AI accelerators such as the Cerebras wafer-scale engine.…”

Section: Resultsmentioning

confidence: 99%

“…Thus, another advantage of the platform model is that we are able to prescribe a common asynchronous REST API and flexible OAuth-based IAM model such that others can implement and integrate external actions with the platform. This API and IAM model could be used to integrate capabilities provided by other cloud-hosted research platforms, such as Tapis 28 and CILogon/CoManage. 80 Integrating other platforms is dependent on the need for platforms to “trust” one another so that authorization decisions can be routed to different authorization servers.…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Linking scientific instruments and computation: Patterns, technologies, and experiences

Vescovi¹,

Chard²,

Saint³

et al. 2022

Patterns

View full text Add to dashboard Cite

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Linking scientific instruments and computation: Patterns, technologies, and experiences

Vescovi¹,

Chard²,

Saint³

et al. 2022

Patterns

View full text Add to dashboard Cite

“…The SambaNova DataScale ® system at the ALCF AI-Testbed uses SambaFlow TM software, which has been integrated with popular open source APIs, such as PyTorch and TensorFlow. Leveraging these tools, we used SambaFlow to automatically extract, optimize, and execute our originally PyTorch BraggNN model with SambaNova's RDUs 38 . We find that the predictions of our SambaNova BraggNN model are consistent with those obtained with PyTorch and TensorRT models.…”

Section: Sambanova Modelmentioning

confidence: 99%

FAIR principles for AI models with a practical application for accelerated high energy diffraction microscopy

Ravi¹,

Chaturvedi²,

Huerta³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

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. Learning from this initiative, and acknowledging the impact of artificial intelligence (AI) in the practice of science and engineering, we introduce a set of practical, concise, and measurable FAIR principles for AI models. We showcase how to create and share FAIR data and AI models within a unified computational framework combining the following elements: the Advanced Photon Source at Argonne National Laboratory, the Materials Data Facility, the Data and Learning Hub for Science, and funcX, and the Argonne Leadership Computing Facility (ALCF), in particular the ThetaGPU supercomputer and the SambaNova DataScale ® system at the ALCF AI Testbed. We describe how this domain-agnostic computational framework may be harnessed to enable autonomous AI-driven discovery.

show abstract

“…Further, the deluge of data generated by scientific devices and the addition of new sensors can quickly exceed the processing capabilities of computing resources collocated with experimental apparatus. Indeed, achieving near-real-time analysis for online feedback may rely on the use of devices throughout the entire computing continuum, from rapid analysis at edge devices and GPU accelerated laboratory servers, to remote high performance computing (HPC) systems and specialized ML accelerators [12].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Streaming and Event-driven Control of Scientific Experiments

R.¹,

Chard²,

Levental³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Advancements in scientific instrument sensors and connected devices provide unprecedented insight into ongoing experiments and present new opportunities for control, optimization, and steering. However, the diversity of sensors and heterogeneity of their data result in make it challenging to fully realize these new opportunities. Organizing and synthesizing diverse data streams in near-real-time requires both rich automation and Machine Learning (ML). To efficiently utilize ML during an experiment, the entire ML lifecycle must be addressed, including refining experiment configurations, retraining models, and applying decisions-tasks that require an equally diverse array of computational resources spanning centralized HPC to the accelerators at the edge. Here we present the Manufacturing Data and Machine Learning platform (MDML). The MDML is designed to standardize the research and operational environment for advanced data analytics and ML-enabled automated process optimization by providing the cyberinfrastructure to integrate sensor data streams and AI in cyber-physical systems for in-situ analysis. To achieve this, the MDML provides a fabric to receive and aggregate IoT data and simultaneously orchestrate remote computation across the computing continuum. In this paper we describe the MDML and show how it is used in advanced manufacturing to act on IoT data and orchestrate distributed ML to guide experiments.

show abstract

Bridging Data Center AI Systems with Edge Computing for Actionable Information Retrieval

Cited by 21 publications

References 29 publications

Linking scientific instruments and computation: Patterns, technologies, and experiences

Linking scientific instruments and computation: Patterns, technologies, and experiences

FAIR principles for AI models with a practical application for accelerated high energy diffraction microscopy

Real-Time Streaming and Event-driven Control of Scientific Experiments

Contact Info

Product

Resources

About