'Iterative refinement' diaporamas de présentation

More Performance!

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Free-Form Deformations

Free-Form Deformations. Free-Form Deformation of Solid Geometric Models Fast Volume-Preserving Free Form Deformation Using Multi-Level Optimization Free-Form Deformations with Lattices of Arbitrary Topology. Introduction - Bezier Curves. Control Polygon Parametric Equations

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Scientific Computing Seminar

Scientific Computing Seminar. AMLS, Spectral Schur Complements and Iterative Computation of Eigenvalues *. * Work supported by NSF under grants NSF/ITR-0082094, NSF/ACI-0305120 and by the Minnesota Supercomputing Institute . C. Bekas: SC Seminar . Introduction and Motivation. Target Problem

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CS 501: Software Engineering

CS 501: Software Engineering. Lecture 2 Software Processes. Administration. Project teams Any short notices to class? Course team email address

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Sparse Matrix Methods

Sparse Matrix Methods. Day 1: Overview Day 2: Direct methods Day 3: Iterative methods The conjugate gradient algorithm Parallel conjugate gradient and graph partitioning Preconditioning methods and graph coloring Domain decomposition and multigrid Krylov subspace methods for other problems

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Concrete Model Checking with Abstract Matching and Refinement

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History of WGA

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Concrete Model Checking with Abstract Matching and Refinement

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Sparse Matrix Methods

Sparse Matrix Methods. Day 1: Overview Day 2: Direct methods Day 3: Iterative methods The conjugate gradient algorithm Parallel conjugate gradient and graph partitioning Preconditioning methods and graph coloring Domain decomposition and multigrid Krylov subspace methods for other problems

374 views • 30 slides

Software Engineering

370 views • 25 slides

CS 501: Software Engineering

CS 501: Software Engineering. Lecture 2 The Software Process. Administration. Project statements Announcements Wednesday evenings Lecture on The Software Process Discussion of Suggested Projects. Some Books. ? Frederick P. Brooks, Jr. The Mythical Man Month. Addison-Wesley, 1972.

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CSE115: Introduction to Computer Science I

CSE115: Introduction to Computer Science I. Dr. Carl Alphonce 219 Bell Hall 645-4739 alphonce@buffalo.edu. Announcements. Cell phones OFF and put away Name signs out Lab 1 starts this week Exam 1 next week (!) Monday: review Wednesday: exam. Problem solving.

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Use of adaptive low-rank approximation in sparse direct solver for 3D Helmholtz problem

Use of adaptive low-rank approximation in sparse direct solver for 3D Helmholtz problem. IPGG SB RAS , Russia, Novosibirsk. Sergey Solovyev. Agenda. Statement of the problem, key points Proposed algorithm Testing results. Statement of the problem , key points.

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Accuracy

Accuracy. Robert Strzodka. Overview. Precision and Accuracy Hardware Resources Mixed Precision Iterative Refinement. Roundoff and Cancellation. Roundoff examples for the float s23e8 format additive roundoff a= 1 + 0.00000004 = fl 1 multiplicative roundoff b= 1.0002 * 0.9998 = fl 1

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ProbCons : Probabilistic consistency based multiple sequence alignment

ProbCons : Probabilistic consistency based multiple sequence alignment. Genome Research 2005 By Serafim Batzoglou et. al Stanford University. Majid Kazemian. Multiple Sequence Alignment. Biologists need accurate tools for multiple sequence alignment to study evolution

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Iterative Refinement of Computational Circuits using Genetic Programming

Iterative Refinement of Computational Circuits using Genetic Programming. Martin A. Keane Econometrics, Inc. Chicago, Illinois makeane@ix.netcom.com. Matthew J. Streeter Genetic Programming Inc. Mountain View, California mjs@tmolp.com. John R. Koza Stanford University

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Symmetric Definite Generalized Eigenproblem

Symmetric Definite Generalized Eigenproblem. Problem: A sym. indefinite and B s.p.d. Standard method in Matlab: Cholesky-QR (optionally QZ) Properties: All eigenvalues should be real Eigenvector matrix diagonalizes both A and B Easy to solve if A, B well-conditioned.

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Lecture #3 Agenda

Lecture #3 Agenda. Cell phones off & name signs out Questions? Activity Problem solving. I have a question for you!. The question was: What did you have for breakfast today?. Activity. The goal of this short activity is to demonstrate two things: objects have behaviors

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Mixed Precision Iterative Refinement Solver Procedure Using High Performance Linpack (HPL)

/* * Find norm_1( A ). */ if( nq > 0 ) { work = (double*)malloc( nq * sizeof( double ) ); if( work == NULL ) { HPL_pabort( __LINE__, "HPL_pdlange", "Memory allocation failed" ); } for( jj = 0; jj < nq; jj++ ) {

105 views • 1 slides