PPT-Parallel Visualization for Very Large Data Simulations

Author : giovanna-bartolotta | Published Date : 2016-06-18

900 AM Sunday June 19 th in Rm 123 Basic usage Data analysis Derived quantities Scripting Moviemaking Comparisons File format readers In situ processing more

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Parallel Visualization for Very Large Data Simulations: Transcript

900 AM Sunday June 19 th in Rm 123 Basic usage Data analysis Derived quantities Scripting Moviemaking Comparisons File format readers In situ processing more Tutorial 08. Theory and Practice:. Large Data and Parallel Visualization. Hank Childs. Lawrence Berkeley National Laboratory /. University of California at Davis. October 25, 2010. Outline. Motivation. Parallelization strategies. Theory and Practice:. Large Data and Parallel Visualization. Hank Childs. Lawrence Berkeley National Laboratory /. University of California at Davis. October 25, 2010. Outline. Motivation. Parallelization strategies. via Interpolation. Seung-Hee. . Bae. , . Jong. Youl Choi, Judy . Qiu. , and Geoffrey Fox. School of Informatics and Computing. Pervasive Technology Institute. Indiana University. S. A. L. S. A. project. Implications . for . Software Environments. . eScience. in the Cloud . 2014. Redmond WA. April 30 2014. Geoffrey . Fox . gcf@indiana.edu. . . http://www.infomall.org. School of Informatics and Computing. Antônio José da Cunha Rodrigues. Tiago Garcia de Senna Carneiro*. TerraLAB. – . Laboratory. for . Earh. System . Modeling. . and. . Simulation. Computer. . Science. . Department. Federal . CloudCom. 2010. Indianapolis, Indiana, USA. Nov 30 – Dec 3, 2010. Azure . MapReduce. AzureMapReduce. A . MapRedue. runtime for Microsoft Azure using Azure cloud services. Azure Compute. Azure BLOB storage for in/out/intermediate data storage. Texas Advanced Computing Center. Napoleon Vs. Russia, 1812-1813. Florence Nightingale Cox Comb. Data Analysis vs. information visualization. Data. Human. Data analysis: . process data to extract knowledge.. Theory and Practice:. Large Data and Parallel Visualization. Hank Childs. Lawrence Berkeley National Laboratory /. University of California at Davis. October 25, 2010. Outline. Motivation. Parallelization strategies. SC 10. New Orleans, USA. Nov 17, 2010. Azure . MapReduce. AzureMapReduce. A . MapRedue. runtime for Microsoft Azure using Azure cloud services. Azure Compute. Azure BLOB storage for in/out/intermediate data storage. Yingcai Xiao. Computation with and without Visual Assistance. 67 x 89 = ? . Visualized Data Analysis. 67. x 89. ---------. 603. + 536. ---------. 5963. . Jay Koven Fall 2013. Research Overview. The problem: Both criminal and Civil investigations are being over with with information in the cyber age.. New techniques are needed to handle the overload. Visualization of data can provide solutions. ParaViewWeb. Web3D – . Paris 2011. Julien. . Jomier. , . Kitware. . julien.jomier@kitware.com. Motivation. Acquiring Data (2). Tissue rotates. Knife advances. This tissue ribbon is collected by a submerged conveyor belt. High-dimensional Data Analysis. Jong Youl Choi, . Seung-Hee. . Bae. , Judy . Qiu. , . and Geoffrey Fox. School of Informatics and Computing. Pervasive Technology Institute. Indiana University. S. A. By:. Dasari Charithambika (210302). Divya Gupta(210353). Course Instructors:. Dr. Preeti Malakar. Dr. Soumya Dutta.. M. Larsen, S. Labasan, P. Navrátil,. J.S. Meredith, and H. Childs (2015). Various hardware architectures are used in supercomputers, including GPUs, many-core coprocessors, large multi-core CPUs, low-power architectures, hybrid designs, and experimental designs..

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