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. Unlike sequential algorithms parallel algorithms cannot be analyzed very well in isolation One of our primary measures of goodness of a parallel system will be its scalability Scalability is the ability of a parallel system to take advantage of incr 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. Xavier . Mancero. Statistics Division, ECLAC. Seminar on poverty measurement. Geneva, 5-6 May 2015. Background. Income provides an incomplete assessment of living standards. . Possible bias characterizing poverty . Andrey Ivanov, Senior Policy Advisor, UNDP BRC. Eschborn. , 14 July 2013. Acknowledgements. This presentation summarizes some preliminary results of an ongoing research based on the data come primarily from . 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. Multicores. Minshu. Zhao. Outline. Introduction. Review of Power management technique. Power management in . Multicore. Identify . Multicores. Characteristics. Apply power management technique. Future of . G. Narayanaswamy. , . P. Balaji. and . W. Feng. Dept. of Comp. Science. Virginia Tech. Mathematics and Comp. Science. Argonne National Laboratory. High-end Computing Trends. High-end Computing (HEC) Systems. Based heavily on a slide set by Colin Ponce. Rethinking Operating System Designs for a . Multicore World. Multicore computer: A computer with more than one CPU.. 1960-1990. : Multicore existed in mainframes . 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. and . Devavrat. Shah. Flowtune. Flowlet. Control for. Datacenter Networks. Response Time: Productivity, Revenue. , Reputation. microservices. . . . develop  network is central. deploy. Gyrokinetic. Particle-to-Grid Interpolation for Multicore Processors. Kamesh. . Madduri. , . Samuel Williams. , . Stephane. . Ethier. , . Leonid . Oliker. , John . Shalf. , Erich . Strohmaier. , Katherine Yelick .

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