PPT-Parallelization of Sparse

Parallelization of Sparse Coding amp Dictionary Learning Univeristy of Colorado Denver Parallel Distributed System Fall 2016 Huynh Manh 11152016 1 Contents Introduction to Sparse Coding Applications of Sparse Representation. Such matrices has several attractive properties they support algorithms with low computational complexity and make it easy to perform in cremental updates to signals We discuss applications to several areas including compressive sensing data stream Kaushik. . Rajan. Abhishek. . Udupa. William Thies. Rigorous Software Engineering. Microsoft Research, India. Parallelization Reconsidered. Are there dependences between loop iterations?. No. Yes. DOALL Parallelism. From Theory to Practice . Dina . Katabi. O. . Abari. , E. . Adalsteinsson. , A. Adam, F. . adib. , . A. . Agarwal. , . O. C. . Andronesi. , . Arvind. , A. . Chandrakasan. , F. Durand, E. . Hamed. , H. . Raja . Giryes. ICASSP 2011. Volkan. Cevher. Agenda. The sparse approximation problem. Algorithms and pre-run guarantees. Online performance guarantees. Performance bound. Parameter selection. 2. Sparse approximation. to Multiple Correspondence . Analysis. G. Saporta. 1. , . A. . . Bernard. 1,2. , . C. . . Guinot. 2,3. 1 . CNAM, Paris, France. 2 . CE.R.I.E.S., Neuilly sur Seine, France. 3 . Université. . François Rabelais. Kaushik. . Rajan. Abhishek. . Udupa. William Thies. Rigorous Software Engineering. Microsoft Research, India. Parallelization Reconsidered. Are there dependences between loop iterations?. No. Yes. DOALL Parallelism. Ron Rubinstein. Advisor: Prof. Michael . Elad. October 2010. Signal Models. Signal models. . are a fundamental tool for solving low-level signal processing tasks. Noise Removal. Image Scaling. Compression. Konstantinos Theodorakos. January 2015. Modern Processor Design. “Free lunch is over”. Lower Power consumption is favored on multi-core/processor architectures. CBD Parallelization . intention. Why Parallelize CBD models?. to Multiple Correspondence . Analysis. G. Saporta. 1. , . A. . . Bernard. 1,2. , . C. . . Guinot. 2,3. 1 . CNAM, Paris, France. 2 . CE.R.I.E.S., Neuilly sur Seine, France. 3 . Université. . François Rabelais. Michael . Elad. The Computer Science Department. The . Technion. – Israel Institute of technology. Haifa 32000, . Israel. David L. Donoho. Statistics Department Stanford USA. . Jeremy Watt and . Aggelos. . Katsaggelos. Northwestern University. Department of EECS. Part 2: Quick and dirty optimization techniques. Big picture – a story of 2’s. 2 excellent greedy algorithms: . Author: . Vikas. . Sindhwani. and . Amol. . Ghoting. Presenter: . Jinze. Li. Problem Introduction. we are given a collection of N data points or signals in a high-dimensional space R. D. : xi ∈ . Yi Ma. 1,2. . Allen Yang. 3. John . Wright. 1. CVPR Tutorial, June 20, 2009. 1. Microsoft Research Asia. 3. University of California Berkeley. 2. University of Illinois . at Urbana-Champaign. Iterative Local Searches. Martin . Burtscher. 1. and Hassan Rabeti. 2. 1. Department of Computer Science, Texas State University-San Marcos. 2. Department of Mathematics, Texas State University-San Marcos.