'Sparse matrices' diaporamas de présentation

An Overview of Trilinos Mark Hoemmen Sandia National Laboratories 30 June 2014

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CS 267 Dense Linear Algebra: History and Structure, Parallel Matrix Multiplication

CS 267 Dense Linear Algebra: History and Structure, Parallel Matrix Multiplication. James Demmel www.cs.berkeley.edu/~demmel/cs267_Spr09. Outline. History and motivation Structure of the Dense Linear Algebra motif Parallel Matrix-matrix multiplication

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CS252 Graduate Computer Architecture Lecture 11 Vectors, Branch Prediction, Dependence Speculation, and Data Prediction

CS252 Graduate Computer Architecture Lecture 11 Vectors, Branch Prediction, Dependence Speculation, and Data Prediction. October 1, 1999 Prof. John Kubiatowicz. SCALAR (1 operation). VECTOR (N operations). v2. v1. r2. r1. +. +. r3. v3. vector length. add.vv v3, v1, v2.

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Supercomputing in Plain English Part IX: Grab Bag

Supercomputing in Plain English Part IX: Grab Bag. Henry Neeman, Director OU Supercomputing Center for Education & Research University of Oklahoma Wednesday November 7 2007. This is an experiment!.

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TECHNIQUES ALGORITHMS AND SOFTWARE FOR THE DIRECT SOLUTION OF LARGE SPARSE LINEAR EQUATIONS

TECHNIQUES ALGORITHMS AND SOFTWARE FOR THE DIRECT SOLUTION OF LARGE SPARSE LINEAR EQUATIONS. Wish to solve Ax = b A is LARGE and S P A R S E. A LARGE . . . ORDER n(t) t n 1970 200 1975 1 000 1980 10 000

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TECHNIQUES ALGORITHMS AND SOFTWARE FOR THE DIRECT SOLUTION OF LARGE SPARSE LINEAR EQUATIONS

TECHNIQUES ALGORITHMS AND SOFTWARE FOR THE DIRECT SOLUTION OF LARGE SPARSE LINEAR EQUATIONS. Wish to solve Ax = b A is LARGE and S P A R S E. A LARGE . . . ORDER n(t) t n 1970 200 1975 1 000 1980 10 000

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Direct Methods for Sparse Linear Systems

Direct Methods for Sparse Linear Systems. Lecture 4 Alessandra Nardi. Thanks to Prof. Jacob White, Suvranu De, Deepak Ramaswamy, Michal Rewienski, and Karen Veroy. Last lecture review. Solution of system of linear equations Existence and uniqueness review Gaussian elimination basics

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Multithreaded Algorithms for Graph Coloring

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Advanced Caches

Advanced Caches. Prof. Mikko H. Lipasti University of Wisconsin-Madison Lecture notes based on notes by John P. Shen and Mark Hill Updated by Mikko Lipasti. Readings. Read on your own: Review: Shen & Lipasti Chapter 3

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CHP-5 LinkedList

CHP-5 LinkedList. 1.INTRODUCTION. In simplest terms, a list refers to a collection of data items of similar type arranged in sequence (that is, one after another). For example, list of students' names, list of addresses.. etc.

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STAR-P: High Productivity Parallel Computing

STAR-P: High Productivity Parallel Computing. David Cheng, Ron Choy, Alan Edelman Massachusetts Institute of Technology John R. Gilbert and Viral Shah University of California at Santa Barbara. Graph Algorithms and Sparse Matrix Land. Birth of Interactive Supercomputing.

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Krishna K Nagar, Jason D. Bakos Heterogeneous and Reconfigurable Computing Lab ( HeRC )

A Sparse Matrix Personality for the Convey HC-1 Dept. of Computer Science and Engineering University of South Carolina. Krishna K Nagar, Jason D. Bakos Heterogeneous and Reconfigurable Computing Lab ( HeRC ) http://herc.cse.sc.edu.

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An Integrated Reduction Technique for a Double Precision Accumulator

An Integrated Reduction Technique for a Double Precision Accumulator. Krishna Nagar, Yan Zhang, Jason Bakos Dept. of Computer Science and Engineering University of South Carolina. Double Precision Accumulation. Many kernels targeted for acceleration include

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CSCE 3110 Data Structures & Algorithm Analysis

CSCE 3110 Data Structures & Algorithm Analysis. Rada Mihalcea http://www.cs.unt.edu/~rada/CSCE3110 More on lists. Circular lists. Doubly linked lists. . Applications of Linked Lists. Stacks and Queues Implemented with Linked Lists Polynomials Implemented with Linked Lists

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Automatic Generation of Vectorized Fast Fourier Transform Libraries for the Larrabee and AVX Instruction Set Exten

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Computer Architecture Parallel Processors

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JAVA AND MATRIX COMPUTATION

JAVA AND MATRIX COMPUTATION. Geir Gundersen Department of Informatics University of Bergen Norway Joint work with Trond Steihaug. Java Grande : Several extension to the Java language has been proposed but NOT integrated or considered by Sun Microsystems.

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CS448f: Image Processing For Photography and Vision

CS448f: Image Processing For Photography and Vision. The Gradient Domain. Image Gradients. We can approximate the derivatives of an image using differences Equivalent to convolution by [-1 1] Note the zero boundary condition. m =. d/dx. dm/dx =. Image Derivatives.

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Avoiding Communication in Sparse Iterative Solvers

Avoiding Communication in Sparse Iterative Solvers. Erin Carson Nick Knight CS294, Fall 2011. Motivation: Sparse Matrices. Many algorithms for scientific applications require solving linear systems of equations: Ax = b. In many cases, the matrix A is sparse

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