PPT-Parallel Multidimensional Scaling Performance on Multicore

Author : debby-jeon | Published Date : 2016-05-17

Community Grids Lab Indiana University Bloomington SeungHee Bae Contents Multidimensional Scaling MDS Scaling by MAjorizing a COmplicated Function SMACOF Parallelization

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Parallel Multidimensional Scaling Performance on Multicore: Transcript

Community Grids Lab Indiana University Bloomington SeungHee Bae Contents Multidimensional Scaling MDS Scaling by MAjorizing a COmplicated Function SMACOF Parallelization of SMACOF Performance Analysis. CCGSC. September 15 . 2008. Geoffrey Fox. Community Grids Laboratory. , . School of informatics . Indiana University. gcf@indiana.edu. , . http://www.infomall.org. 2. Gartner 2008 Technology Hype Curve. input data are ranks: most similar pair AB. <. CB. <. AC most different pair, or input data are rating scales: (very similar 1. . .9 very different), but we don't believe in assuming interval level property of data.. ISC 2015. Kenneth Moreland . and. Ron Oldfield. Sandia National Laboratories. Parallel Algorithm Speedup. Parallel Algorithm Speedup. Serial time for large problem sizes. Cannot be measured in practice. Majorization. Algorithm. Seung-Hee. . Bae. , Judy . Qiu. , and Geoffrey Fox. SALSA group in Pervasive Technology Institute. at Indiana University Bloomington. Outline. Motivation. Background and Related Work. Andrew Jaffe. Computing Club. 4/5/2015. Overview. Introduction. multicore . Array jobs. The rest. Introduction. Based roughly on: McCallum and Weston. Parallel R. 2012 (O’Reilly Book), so consult for the more complicated methods. Kruskal. , Joseph B. and Myron Wish (1978), . Multidimensional Scaling. , Beverly Hills: Sage. . Great primer to get you started.. Green, Paul E., Frank J. . Carmone. Jr., and Scott M. Smith (1989), . Dimension Reduction. Student: . Seung-Hee. . Bae. Advisor: . Dr. Geoffrey C. Fox. School of Informatics and Computing. Pervasive Technology Institute. Indiana University. Thesis Defense, Jan. 17, 2012. 6/16/2010. Parallel Performance. 1. Performance Considerations. I parallelized my code and …. The code slower than the sequential version, or. I don’t get enough speedup . What should I do?. 6/16/2010. CCGSC. September 15 . 2008. Geoffrey Fox. Community Grids Laboratory. , . School of informatics . Indiana University. gcf@indiana.edu. , . http://www.infomall.org. 2. Gartner 2008 Technology Hype Curve. Multicore Panel at eScience 2008. December . 11 2008. Geoffrey Fox. Community Grids Laboratory. , . School of informatics . Indiana University. gcf@indiana.edu. , . http://www.infomall.org. Lessons. Not surprising . Ananth Grama. . Computer Science, Purdue University. ayg@cs.purdue.edu. Outline. Introduction. Molecular Dynamics Methods vs. ReaxFF. Sequential Realization: SerialReax. Algorithms and Numerical Techniques. Ali S. Hadi and . Rida. Moustafa. ahadi@aucegypt.edu. ali-hadi@cornell.edu. www.aucegypt.edu/faculty/hadi. Outline of the Talk. Introduction. 2. Types of . Centering and/or Scaling. . Effects of Centering and/or Scaling. Gyrokinetic. Particle-to-Grid Interpolation for Multicore Processors. Kamesh. . Madduri. , . Samuel Williams. , . Stephane. . Ethier. , . Leonid . Oliker. , John . Shalf. , Erich . Strohmaier. , Katherine Yelick . Osman . Sarood. How faster can we run?. Suppose we have this serial problem with 12 tasks. How fast can we run given 3 processors?. Running in parallel. Execution time reduces from 12 . secs. to 4 .

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