scholarly journals Final Report: Sublinear Algorithms for In-situ and In-transit Data Analysis at Exascale.

2015 ◽  
Author(s):  
Janine Camille Bennett ◽  
Ali Pinar ◽  
C. Seshadhri ◽  
David Thompson ◽  
Maher Salloum ◽  
...  
Keyword(s):  
Data Analysis ◽  
Final Report ◽  
Sublinear Algorithms ◽  
In Transit

Summary of the SciTech 2020 Technical Panel on In Situ/In Transit Computational Environments for Visualization and Data Analysis

2021 ◽  
Author(s):  
Earl Duque ◽  
Steve Legensky ◽  
Brad Whitlock ◽  
David Rogers ◽  
Andrew Bauer ◽  
...  
Keyword(s):  
Data Analysis ◽  
Cfd Simulation ◽  
Fluid Dynamic ◽  
Technical Committee ◽  
Department Of Energy ◽  
Memory Space ◽  
Vision 2030 ◽  
In Transit ◽  
Japan Aerospace Exploration Agency

At the AIAA SciTech 2020 conference, the Meshing, Visualization and Computational Environments Technical Committee hosted a special technical panel on In Situ/In Transit Computational Environments for Visualization and Data Analytics. The panel brought together leading experts from industry, software vendors, Department of Energy, Department of Defense and the Japan Aerospace Exploration Agency (JAXA). In situ and in transit methodologies enable Computational Fluid Dynamic (CFD) simulations to avoid the excessive overhead associated with data I/O at large scales especially as simulations scale to millions of processors. These methods either share the data analysis/visualization pipelines with the memory space of the solver or efficiently off load the workload to alternate processors. Using these methods, simulations can scale and have the promise of enabling the community to satisfy the Knowledge Extraction milestones as envisioned by the CFD Vision 2030 study for "on demand analysis/visualization of a 100 Billion point unsteady CFD simulation". This paper summarizes the presentations providing a discussion point of how the community can achieve the goals set forth in the CFD Vision 2030.

Summary of the SciTech 2020 Technical Panel on In Situ/In Transit Computational Environments for Visualization and Data Analytics

2021 ◽  
Author(s):  
Earl P. Duque ◽  
Steve M. Legensky ◽  
Brad J. Whitlock ◽  
David H. Rogers ◽  
Andrew C. Bauer ◽  
...  
Keyword(s):  
Data Analytics ◽  
In Transit

scholarly journals Data analysis methods adopted under in situ global project in Nepal

1970 ◽  
Vol 6 ◽  
pp. 98-108
Author(s):  
Bal K Joshi ◽  
Madhusudan P Upadhyay ◽  
Hari P Bimb ◽  
D Gauchan ◽  
BK Baniya
Keyword(s):  
Data Analysis ◽  
Experimental Design ◽  
Seed Systems ◽  
Statistical Tools ◽  
Crop Species ◽  
Analysis Methods ◽  
On Farm ◽  
Global Project ◽  
Data Analysis Methods

Synthesizing data analysis methods adopted under in situ global project in Nepal along withvariables and nature of study could be guiding reference for researchers especially to those involvedin on farm research. The review work was conducted with the objective to help in utilizing andmanaging in situ database system. The objectives of the experiment, the structure of the treatmentsand the experimental design used primarily determine the type of analysis. There were 60 papers ofthis project published in Nepal. All these papers are grouped under 8 thematic groups namely 1.Agroecosystem (3 papers), 2. Agromorphological and farmers’ perception (7 papers), 3. Croppopulation structure (5 papers), 4. Gender, policy and general (15 papers), 5. Isozyme andmolecular (6 papers), 6. Seed systems and farmers’ networks (5 papers), 7. Social, cultural andeconomical (11 papers) and 8. Value addition (8 papers). All these papers were reviewed basicallyfor data type, sample size, sampling methods, statistical methods and tools, varieties and purposes.Descriptive and inferential statistics along with multivariate methods were commonly used in onfarm research. Experimental design, the most common in on station trial was least used. Study overspace and time was not adopted. There were 5 kinds of data generated, 45 statistical tools adoptedin eight different crop species. Among the 5 kinds of data under these eight subject areas,categorical type was highest followed by discrete numerical. Binary type was least in frequency.Most of the papers were related to rice followed by taro and finger millet. Cucumber and pigeonpea were studied least. Descriptive statistics along with Χ2, multivariate analysis and regressionapproaches would be appropriate tools. Similarly SPSS and MINITAB may be good software. Thebest one among a number of statistical tools should be selected and utmost care must be exercisedwhile collecting data.Key words: Data analysis methods; on farm research; on station research; subject areasDOI: 10.3126/narj.v6i0.3371Nepal Agriculture Research Journal Vol.6 2005 pp.98-108

scholarly journals Liquid Argon Instrumentation and Monitoring in LEGEND-200

2021 ◽  
Vol 253 ◽  
pp. 11014
Author(s):  
Mario Schwarz ◽  
Patrick Krause ◽  
Andreas Leonhardt ◽  
Laszlo Papp ◽  
Stefan Schönert ◽  
...  
Keyword(s):  
Data Analysis ◽  
Liquid Argon ◽  
Neutrinoless Double Beta Decay ◽  
Double Beta Decay ◽  
Optical Parameters ◽  
Water Tank ◽  
Light Collection ◽  
Wavelength Shifting Fibers ◽  
Detection Medium

LEGEND is the next-generation experiment searching for the neutrinoless double beta decay in 76Ge. The first stage, LEGEND-200, takes over the cryogenic infrastructure of GERDA at LNGS: an instrumented water tank surrounding a 64 m3 liquid argon cryostat. Around 200 kg of Ge detectors will be deployed in the cryostat, with the liquid argon acting as cooling medium, high-purity passive shielding and secondary detection medium. For the latter purpose, a liquid argon instrumentation is developed, based on the system used in GERDA Phase II. Wavelength shifting fibers coated with TPB are arranged in two concentric barrels. Both ends are read out by SiPM arrays. A wavelength shifting reflector surrounds the array in order to enhance the light collection far from the array. The LLAMA is installed in the cryostat to permanently monitor the optical parameters and to provide in-situ inputs for modeling purposes. The design of all parts of the LEGEND-200 LAr instrumentation is presented. An overview of the geometry, operation principle, and off-line data analysis of the LLAMA is shown.

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