PPT-An Efficient Parallel Solver for SDD Linear Systems

Richard Peng MIT Joint work with Dan Spielman Yale Efficient Parallel Solvers for SDD Linear Systems Richard Peng MIT Work in progress with Dehua Cheng USC Yu Cheng USC Yintat. Unlike sequential algorithms parallel algorithms cannot be analyzed very well in isolation One of our primary measures of goodness of a parallel system will be its scalability Scalability is the ability of a parallel system to take advantage of incr Lecturer: . Qinsi. Wang. May 2, 2012. Z3. high-performance theorem . prover. being developed at Microsoft Research.. mainly by Leonardo de . Moura. and . Nikolaj. . Bjørner. . . Free (online interface, APIs, …) . Multiphysics. HPC Applications . CS267 . – Spring 2013. John Shalf. With contributions from: . Gabrielle Allen, Tom . Goodale. , Eric . Schnetter. , Ed Seidel (AEI/LSU). Phil . Colella. , Brian Van . Circulant. Linear Systems with Applications to Acoustics. Suzanne Shontz, University of Kansas . Ken . Czuprynski. , University of . Iowa. John . Fahnline. , Penn State. EECS 739: Scientific Parallel Computing. Richard Peng. M.I.T.. Joint work with Dan Spielman (Yale). Efficient Parallel Solvers for SDD Linear Systems. Richard Peng. M.I.T.. Work in progress with . Dehua. Cheng (USC),. Yu Cheng (USC), . Yintat. Circulant. Linear Systems with Applications to Acoustics. Suzanne Shontz, University of Kansas . Ken . Czuprynski. , University of Iowa. John . Fahnline. , Penn State. EECS 739: Scientific Parallel Computing. Dr. Ron Lembke. Formulating in Excel. Write the LP out on paper, with all constraints and the objective function.. Decide on cells to represent variables.. Enter coefficients of each variable in each constraint in a block of cells.. An optimization problem is a problem in which we wish to determine the best values for decision variables that will maximize or minimize a performance measure subject to a set of constraints. A feasible solution is set of values for the decision variables which satisfy all of the constraints. 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 . Circulant. Linear Systems with Applications to Acoustics. Suzanne Shontz, University of Kansas . Ken . Czuprynski. , University of Iowa. John . Fahnline. , Penn State. EECS 739: Scientific Parallel Computing. Circulant. Linear Systems with Applications to Acoustics. Suzanne Shontz, University of Kansas . Ken . Czuprynski. , University of . Iowa. John . Fahnline. , Penn State. EECS 739: Scientific Parallel Computing. A Brown Bag discussion for N-81. 26 Sept 2012. THIS PRESENTATION IS UNCLASSIFIED. Purpose. This Talk promises to:. (re)introduce some powerful tools in Excel. Optimization – centric functions. Goal seek. FEKOBy offering a selection of different solvers FEKO users can choose the method that is most suitable to the problem that they are trying to solve or use more than one solver for cross validation pu Vida . Rozite. . The Role of Standardization for Smart Grids in Realizing Their Energy-Efficiency Potential and Their Enabling Effect in Developing Access to Electricity in the Third World. Benefits .