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. for the NCEP GFS. Tom Hamill, for . Jeff . Whitaker. NOAA Earth System Research Lab, Boulder, CO, USA. jeffrey.s.whitaker@noaa.gov. Daryl Kleist, Dave Parrish and John . Derber. National Centers for Environmental Prediction, Camp Springs, MD, USA. Ensemble Clustering. unlabeled . data. ……. F. inal . partition. clustering algorithm 1. combine. clustering algorithm . N. ……. clustering algorithm 2. Combine multiple partitions of . given. data . Ludmila. I . Kuncheva. School of Computer Science. Bangor University, UK. Are we still talking about diversity in classifier ensembles?. Ludmila. I . Kuncheva. School of Computer Science. Bangor University, UK. 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. and post-processing . team reports to NGGPS. Tom Hamill. ESRL, Physical Sciences Division. tom.hamill@noaa.gov. (303) 497-3060. 1. Proposed team . members. Ensemble system development. Post-processing. Molly Smith, Ryan Torn, . Kristen . Corbosiero. , and Philip . Pegion. NWS Focal Points: . Steve . DiRienzo. . and Mike . Jurewicz. . WFO . BGM Sub-Regional Workshop . 23 September, 2015. Motivation. Which of the two options increases your chances of having a good grade on the exam? . Solving the test individually. Solving the test in groups. Why?. Ensemble Learning. Weak classifier A. Ensemble Learning. Applying data assimilation for rapid forecast updates in global weather models. Luke E. Madaus --- Greg Hakim; Cliff Mass. University of Washington. In Revision -- QJRMS. Outline. Brief introduction. Vasilios Mitrokostas. 2. An introduction to Internet Telephony. How VoIP works. Modern VoIP implementations. VoIP in massively multiplayer online gaming. Final thoughts. 2. Voice over Internet Protocol. 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 . Dongsheng. Luo, Chen Gong, . Renjun. Hu. , Liang . Duan. Shuai. Ma, . Niannian. Wu, . Xuelian. Lin. TeamBUAA. Problem & Challenges. Problem: . rank nodes in a heterogeneous graph based on query-independent node importance . Better Predictions Through Diversity. Todd Holloway. ETech 2008. Outline. Building a classifier (a tutorial example). Neighbor method. Major ideas and challenges in classification. Ensembles in practice. Bright, . Colle. , . DiMego. , Hacker, Whitaker. 22 Aug. 2012. DTC SAB ensemble task. 1. Primary recommendation. Continue to pursue long-term goal of pivotal and more tangible role in research-to-operations (R2O) transitions. .