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Monday, May 18, 2020 | History

4 edition of Parallel-vector unsymmetric Eigensolver on high performance computers found in the catalog.

Parallel-vector unsymmetric Eigensolver on high performance computers

Parallel-vector unsymmetric Eigensolver on high performance computers

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Published by National Aeronautics and Space Administration, Langley Research Center, National Technical Information Service, distributor in Hampton, Va, [Springfield, Va .
Written in English


Edition Notes

Other titlesParallel vector unsymmetric Eigensolver on ...
StatementDuc T. Nguyen and Qin Jiangning.
SeriesNASA contractor report -- 191417., NASA contractor report -- NASA CR-191417.
ContributionsJiangning, Qin., Langley Research Center.
The Physical Object
FormatMicroform
Pagination1 v.
ID Numbers
Open LibraryOL17113380M
OCLC/WorldCa33070515

Computing planetary interior normal modes with a highly parallel polynomial filtering eigensolver. Share on. for Large Scale-free Graphs Using 2D Graph Partitioning," in Proceedings of International Conference for High Performance "Multifrontal parallel distributed symmetric and unsymmetric solvers," Computer methods in applied. To sum up, our sparse matrix-vector multiplication is able to gain similar or even better performance on large generated or real-world graphs compared with the sparse matrix-vector multiplication provided by cuSparse. Finally, we show the comparison of eigensolver running time for our CUDA Lanczos implementation and sequential CPU implementation.

A Parallel Implementation of the Jacobi-Davidson Eigensolver for Unsymmetric Matrices. High Performance Computing for Computational Science – VECPAR , () From Efficient Symplectic Exponentiation of Matrices to Symplectic Integration of High-dimensional Hamiltonian Systems with Slowly Varying Quadratic Stiff by: A parallel unsymmetric eigensolver. G. Henry; R. van de Geijn. Publication Year: , Page High performance communication in the gigabit range can cause the end host to be the bottleneck. This fluctuation may hinder efficient utilization of distributed memory parallel computers because of the resulting overhead for data redistribution.

A Parallel Implementation of the Jacobi-Davidson Eigensolver for Unsymmetric Matrices? Eloy Romero1, Manuel B. Cruz2, Jose E. Roman1, and Paulo B. Vasconcelos3 1 Instituto I3M, Universidad Polit ecnica de Valencia, Camino de Vera s/n, Valencia, Spain feromero,[email protected] The archetypical level 3 routine is the dgemmoperation in which an m x n matrix A is updated by the product of matrices U and VT of respective sizes m x k and k x n (A = A + UV~). Typically, k is much smaller than m and n, but making it larger than 1 allows for cache reuse and greater overall performance.


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Parallel-vector unsymmetric Eigensolver on high performance computers Download PDF EPUB FB2

PARALLEL-VECTOR UNSYMMETRIC EIGEN-SOLVER ON HIGH PERFORMANCE COMPUTERS Duc T. Nguyen Qin Jiangning Center for Multidisciplinary Parallel-Vector Computation Civil Engineering Department Old Dominion University Abstract The popular QR algorithm for solving all eigenvalues of an unsymmetric matrix is reviewed.

Get this from a library. Parallel-vector unsymmetric Eigensolver on high performance computers book unsymmetric Eigensolver on high performance computers.

[Duc T Nguyen; Qin Jiangning; Langley Research Center.]. Interest in Eigensolver Performance •Demand for efficient eigensolver performance –Traditionally a huge computational bottleneck •Especially Electronic Structure codes, Atomic Molecular Codes, large-scale problems.

–At STFC this includes PRMAT, CRYSTAL, GAMESS-UK, KPPW •Large amounts of memory, cpu time and communication required. The popular QR algorithm for solving all eigenvalues of an unsymmetric matrix is reviewed. Among the basic components in the QR algorithm, it was concluded from this study, that the reduction of an unsymmetric matrix to a Hessenberg form (before applying the QR algorithm itself) can be done effectively by exploiting the vector speed and multiple processors offered by modern high-performance : Duc T.

Nguyen and Qin Jiangning. Agarwal, T.K., O.O. Storaasli and D.T. Nguyen, “A Parallel-Vector Algorithm for Rapid Structural Analysis on High-Performance Computers,” Proceedings of the 31 st Structures, Structural Dynamics and Materials Conference, Long Beach, California, pp Author: Due Thai Nguyen.

from book High performance computing for computational science – VECPAR 9th international conference, Berkeley, CA, USA, June 22–25. High Performance Compilers for Parallel Computing provides a clear understanding of the analysis and optimization methods used in modern commercial research compilers for parallel systems.

By the author of the classic monograph Optimizing Supercompilers for Supercomputers, this book covers the knowledge and skills necessary to build a competitive, advanced compiler for parallel or high Cited by: A Parallel Implementation of the Jacobi-Davidson Eigensolver for Unsymmetric Matrices.

High Performance Computing for Computational Science – VECPAR() A parallel additive Schwarz preconditioned Jacobi–Davidson algorithm for polynomial eigenvalue problems in quantum dot by: A parallel-vector unsymmetric equation solver is presented.

The solver exploits both vector and parallel capabilities provided by modern, high-perform Cited by: The non-linear (large deflection and non-linear aerodynamics) 3 D panel flutter analysis (as shown in Fig.

4) by the finite element method similar to the method proposed in Mei and Gray5 for 2 D panels, is used to evaluate the performance of the proposed parallel-vector unsymmetric equation solver for engineering applications on super- by: Abstract.

This paper describes a parallel implementation of the Jacobi-Davidson method to compute eigenpairs of large unsymmetric matrices. Taking advantage of the capabilities of the PETSc library —Portable Extensible Toolkit for Scientific Computation—, we build an efficient and robust code adapted either for traditional serial computation or parallel computing by: 1.

Computational algorithms for structural analysis on parallel-vector supercomputers are reviewed. These parallel algorithms, developed by the authors, are for the assembly of structural equations, “out-of-core” strategies for linear equation solution, massively distributed-memory equation solution, unsymmetric equation solution, general eigen-solution, geometrically nonlinear finite Cited by: T.

Agarwal, O. Storaasli and D. Nguyen, "A parallel-vector algorithm for rapid structural analysis on high-performance computers," Proceedings of the AIAA/ASME/ASCE/AHS 31st Structures, Structural Dynamics and Materials Conference, Paper No. ILong Beach, CA, 2~4 April, Nonlinear vector eigen-solver 6.

: D.Y. Xue, Chuh Mei. Download Citation | Parallel-Vector Unsymmetrical Equation Solver | Unsymmetric matrices are not uncommon in large-scale structural analysis. In. Effect of more processors on analysis time (high- speed research aircraft). High speed research aircraft To evaluate the performance of the parallel-vector Choleski solver, a structural static analysis has been performed on a 16, degree-of-freedom finite- element model of a high-speed aircraft concept [22], Cited by: 5.

The process is called SIMD. Single instruction, multiple data (SIMD), is a class of parallel computers in Flynn's taxonomy. It describes computers with multiple processing elements that perform the same operation on multiple data simultaneously.

Thus, such machines exploit data level parallelism. Abstract. An implementation of a real symmetric eigensolver on parallel nodes is described and evaluated.

To achieve better performance in the inverse iteration part, a multi-color framework is introduced, in which the orders of the orthogonalizations are rescheduled so that the inverse iterations are executed by: 1.

Computing Planetary Interior Normal Modes with a Highly Parallel Polynomial Filtering Eigensolver Conference Paper (PDF Available) November. A Parallel Implementation of the Jacobi-Davidson Eigensolver for Unsymmetric Matrices.

High Performance Computing for Computational Science – VECPAR() Compact integration factor methods for complex domains and adaptive mesh by: A refined unsymmetric Lanczos eigensolver for computing accurate eigentriplets of a real unsymmetric matrix Article (PDF Available) in Electronic transactions on.

In this algorithm, a tradeoff exists between speed and memory space to keep the Householder vectors. As a result of a performance evaluation with the T2K Open Supercomputer (U.

Tokyo) and the RXS5, we obtain the performance with x and x speed-downs and 1/2 memory space compared to the conventional algorithm for a square process by: 5.The workshop held at the High Performance Computing Center Stuttgart (HLRS) was the second of this kind.

Direct Numerical Simulation of Shear Flow Phenomena on Parallel Vector Computers. Pages Babucke, Andreas (et al.) Preview. High Performance Computing on Vector Systems Book Subtitle.A new eigensolver for dense real-symmetric matrices resulting from upgrading and combining previous programs is tested for matrices of orders to Author: Carlos Bunge.