PPT-Massively Parallel Ensemble Methods Using Work Queue

Badi AbdulWahid Department of Computer Science University of Notre Dame CCL Workshop 2012 Overview Background Challenges and approaches Our Work Queue software FoldingWork FW Accelerated Weighted Ensemble AWE. Gupta IBM India Research Labs Block 1 IIT Hauz Khas New Delhi India saurabhagarwal grahul meetashainibmcom Jose E Moreira IBM TJ Watson Research Center Yorktown Heights NY 10598 moreirausibmcom ABSTRACT Giventhescaleofmassivelyparallelsystemsoccurre Boosting, Bagging, Random Forests and More. Yisong Yue. Supervised Learning. Goal:. learn predictor h(x) . High accuracy (low error). Using training data {(x. 1. ,y. 1. ),…,(. x. n. ,y. n. )}. Person. Ensemble Clustering. unlabeled . data. ……. F. inal . partition. clustering algorithm 1. combine. clustering algorithm . N. ……. clustering algorithm 2. Combine multiple partitions of . given. data . Zur. Computational Physics. Differential Equations. Autumn Colors. , by . Bobby . Mikul. , http://www.publicdomainpictures.net. Version 10-11-2010 18:30. Agenda. MHJ Chapter 13 & . Koonin. Chapter 2. Goals for Rest of Course. Learn how to program massively parallel processors and achieve. high performance. functionality and maintainability. scalability across future generations. Acquire technical knowledge required to achieve the above goals. CUDA Lecture 1. Introduction to Massively Parallel Computing. A quiet revolution and potential buildup. Computation: TFLOPs . vs. . 100 GFLOPs. CPU in every PC – massive volume and potential impact. Richard Peng. M.I.T.. Joint work with Dan Spielman (Yale). Efficient Parallel Solvers for SDD Linear Systems. Richard Peng. M.I.T.. Work in progress with . Dehua. Cheng (USC),. Yu Cheng (USC), . Yintat. Lifeng. Yan. 1361158. 1. Ensemble of classifiers. Given a set . of . training . examples, . a learning algorithm outputs a . classifier which . is an hypothesis about the true . function f that generate label values y from input training samples x. Given . Richard Peng. M.I.T.. Joint work with Dan Spielman (Yale). Efficient Parallel Solvers for SDD Linear Systems. Richard Peng. M.I.T.. Work in progress with . Dehua. Cheng (USC),. Yu Cheng (USC), . Yintat. Strategies for Scaling Up Applications. Douglas . Thain. University of Notre Dame. Institute for Computational Economics. University of Chicago. 27 July 2012. The Cooperative Computing Lab. The Cooperative Computing Lab. Karol Kowalski. William R Wiley Environmental Molecular Sciences Laboratory and Chemical Sciences Division,. Pacific Northwest National Laboratory. How it started. Coester. & Kummel (1958,1960). February 26, 2021. Epidemiology and Biostatistics. Introduction. An ensemble model is essentially a combination of models, each using different variables or different priors for variables.. 1. Ensemble modeling is a group of techniques and so there are many different types of ensemble models.. The Desired Brand Effect Stand Out in a Saturated Market with a Timeless Brand The Desired Brand Effect Stand Out in a Saturated Market with a Timeless Brand