PPT-Sparse Matrix Algorithms on GPUs and their Integration into

Author : debby-jeon | Published Date : 2016-03-07

amp Miriam Leeser Dana Brooks melcoeneuedu brookseceneuedu 1 This work is supported by NSF CenSSIS The Center for Subsurface Sensing and Imaging

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Sparse Matrix Algorithms on GPUs and their Integration into: Transcript

amp Miriam Leeser Dana Brooks melcoeneuedu brookseceneuedu 1 This work is supported by NSF CenSSIS The Center for Subsurface Sensing and Imaging. Least Absolute Shrinkage via . The . CLASH. Operator. Volkan. Cevher. Laboratory. for Information . . and Inference Systems – . LIONS / EPFL. http://lions.epfl.ch . . & . Idiap. Research Institute. 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. Aditya. Chopra and Prof. Brian L. Evans. Department of Electrical and Computer Engineering. The University of Texas at Austin. 1. Introduction. Finite Impulse Response (FIR) model of transmission media. Tianzhu . Zhang. 1,2. , . Adel Bibi. 1. , . Bernard Ghanem. 1. 1. 2. Circulant. Primal . Formulation. 3. Dual Formulation. Fourier Domain. Time . Domain. Here, the inverse Fourier transform is for each . Michael . Elad. The Computer Science Department. The . Technion. – Israel Institute of technology. Haifa 32000, . Israel. David L. Donoho. Statistics Department Stanford USA. Matrix. •. . Binary Matrix. •. . Sparse Matrix. •. . Operations for Vectors/Matrices. •. . Graph and Adjacent Matrix. •. . Adjacent List. Matrix and Graph. •. . Matrix is a 2-dimensional . Contents. Problem Statement. Motivation. Types . of . Algorithms. Sparse . Matrices. Methods to solve Sparse Matrices. Problem Statement. Problem Statement. The . solution . of . the linear system is the values of the unknown vector . 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. Applications. Lecture 5. : Sparse optimization. Zhu Han. University of Houston. Thanks Dr. . Shaohua. Qin’s efforts on slides. 1. Outline (chapter 4). Sparse optimization models. Classic solvers and omitted solvers (BSUM and ADMM). Richard Peng. Georgia Tech. OUtline. (Structured) Linear Systems. Iterative and Direct Methods. (. Graph) . Sparsification. Sparsified. Squaring. Speeding up Gaussian Elimination. Graph Laplacians. 1. Shi & Bo. What is sparse system. A system of linear equations is called sparse if . only a relatively small . number of . its matrix . elements . . are nonzero. It is wasteful to use general methods . John R. Gilbert (. gilbert@cs.ucsb.edu. ). www.cs.ucsb.edu/~gilbert/. cs219. Systems of linear equations:. . Ax = . b. Eigenvalues and eigenvectors:. Aw = . λw. Systems of linear equations: Ax = b. Matrix Profile II: Exploiting a Novel Algorithm and GPUs to break the one Hundred Million Barrier for Time Series Motifs and Joins Yan Zhu, Zachary Zimmerman, Nader Shakibay Senobari Chin-Chia Michael Yeh, Gareth Funning, Abdullah Mueen, Jim . Demmel. EECS & Math Departments. UC Berkeley. Why avoid communication? . Communication = moving data. Between level of memory hierarchy. Between processors over a network. Running time of an algorithm is sum of 3 terms:.

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