PPT-IU Twister Supports Data Intensive

Author : lois-ondreau | Published Date : 2016-08-01

Science Applications httpsalsahpcindianaedu School of Informatics and Computing Indiana University Application Classes 1 Synchronous Lockstep Operation as in SIMD

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IU Twister Supports Data Intensive: Transcript

Science Applications httpsalsahpcindianaedu School of Informatics and Computing Indiana University Application Classes 1 Synchronous Lockstep Operation as in SIMD architectures SIMD 2. Supports have been designed for water closets urinals lavatories sinks drinking fountains and many special purpose hospital institutional and industrial plumbing 64257xtures DEFINITION Fixtures which are wall mounted and have no external contact wit NSF Cloud PI Workshop. March 17, 2011. . Judy Qiu. http://salsahpc.indiana.edu . School of Informatics and Computing. Indiana University. Twister . v0.9. March 15, 2011. New . Infrastructure . for . Overview . Rationale . for . intensive intervention. Introduction . to . Data-Based Individualization (DBI). Academic illustration. Behavior illustration. Wrap-up. Today’s Presentation . 2. Learning Objectives. 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. Jaliya Ekanayake. Community Grids Laboratory, . Digital Science Center. Pervasive Technology Institute. Indiana University. HPDC – 2010 MAPREDUCE’10 Workshop, Chicago, 06/22/2010. Acknowledgements to:. Data Enabled . S. cience. S. A. L. S. A. . HPC Group . http://. salsahpc.indiana.edu. School of Informatics and Computing. Indiana University. Bioinformatics Pipeline. Gene Sequences (N = 1 Million). Overview . Rationale . for . intensive intervention. Introduction . to . Data-Based Individualization (DBI). Academic illustration. Behavior illustration. Wrap-up. Today’s Presentation . 2. Learning Objectives. Mega 10^6. Giga 10^9. Tera. 10^12. Peta. 10^15. Pig Latin. Understanding . MapReduce. . in Context. Linux HPC. Bare-system. Amazon Cloud. Windows Server HPC. Bare-system. Virtualization. Cross Platform Iterative . S Jha. 1. , J Qiu. 2. , A Luckow. 1. , P Mantha. 1. , Geoffrey Fox. 2. 1 . Rutgers http://radical.rutgers.edu. 2 . Indiana . . http. ://. www.infomall.org. http://. arxiv.org. /abs/1403.1528. Redesign of Long Term Care . Empower and encourage work, housing and independence. Redesign home and . community-based . services. Coordinate and streamline services for people with complex needs. Implement and further refine Nursing Facility Level of Care. Redesign of Long Term Care . Empower and encourage work, housing and independence. Redesign home and . community-based . services. Coordinate and streamline services for people with complex needs. Implement and further refine Nursing Facility Level of Care. S. cience. S. A. L. S. A. . HPC Group . http://. salsahpc.indiana.edu. School of Informatics and Computing. Indiana University. Bioinformatics Pipeline. Gene Sequences (N = 1 Million). Distance Matrix. Computations. K-means. Performance of K-Means. Smith Waterman is a non iterative case and of course runs fine. Matrix Multiplication . 64 cores. Square blocks Twister. Row/Col . decomp. Twister. nabling HPC-Cloud Interoperability. S. A. L. S. A. . HPC Group . http://salsahpc.indiana.edu. School of Informatics and Computing. Indiana University. Workshop on . Petascale. Data Analytics: Challenges, and .

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