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While experiments on fusion plasmas produce high-dimensional data time series with ever increasing magnitude and velocity, data analysis has been lagging behind this development. For example, many data analysis tasks are often performed in a manual, ad-hoc manner some time after an experiment. In this article we introduce the DELTA framework that facilitates near real-time streaming analysis of big and fast fusion data. By streaming measurement data from fusion experiments to a high-performance compute center, DELTA allows to perform demanding data analysis tasks in between plasma pulses. This article describe the modular and expandable software architecture of DELTA and presents performance benchmarks of its individual components as well as of entire workflows. Our focus is on the streaming analysis of ECEi data measured at KSTAR on NERSCs supercomputers and we routinely achieve data transfer rates of about 500 Megabyte per second. We show that a demanding turbulence analysis workload can be distributed among multiple GPUs and executes in under 5 minutes. We further discuss how DELTA uses modern database systems and container orchestration services to provide web-based real-time data visualization. For the case of ECEi data we demonstrate how data visualizations can be augmented with outputs from machine learning models. By providing session leaders and physics operators results of higher order data analysis using live visualization they may monitor the evolution of a long-pulse discharge in near real-time and may make more informed decision on how to configure the machine for the next shot.
X-ray scattering experiments using Free Electron Lasers (XFELs) are a powerful tool to determine the molecular structure and function of unknown samples (such as COVID-19 viral proteins). XFEL experiments are a challenge to computing in two ways: i)
This paper develops an incremental learning algorithm based on quadratic inference function (QIF) to analyze streaming datasets with correlated outcomes such as longitudinal data and clustered data. We propose a renewable QIF (RenewQIF) method within
We present here Nested_fit, a Bayesian data analysis code developed for investigations of atomic spectra and other physical data. It is based on the nested sampling algorithm with the implementation of an upgraded lawn mower robot method for finding
Electric signals have been recently recorded at the Earths surface with amplitudes appreciably larger than those hitherto reported. Their entropy in natural time is smaller than that, $S_u$, of a ``uniform distribution. The same holds for their entro
Recently, two novel techniques for the extraction of the phase-shift map (Tomassini {it et.~al.}, Applied Optics {bf 40} 35 (2001)) and the electronic density map estimation (Tomassini P. and Giulietti A., Optics Communication {bf 199}, pp 143-148 (2