Parallel scheduling of the PCG method for banded matrices rising from FDM/FEM

An implicit time-linearized finite difference discretization of partial differential equations on regular/structured meshes results in an n-diagonal block system of algebraic equations, which is usually solved by means of the Preconditioned Conjugate Gradient (PCG) method. In this paper, an analysis of the parallel implementation of this method on several computer architectures and for several programming paradigms is presented. For three-dimensional regular/structured meshes, a new implementation of the PCG method with Jacobi preconditioner is proposed. For the computer architectures and number of processors employed in this study, it has been found that this implementation is more efficient than the standard one, and can be applied to narrow-band matrices and other preconditioners, such as, for example, polynomial ones.

[1]  Anthony T. Chronopoulos,et al.  s-step iterative methods for symmetric linear systems , 1989 .

[2]  Eva M. Ortigosa,et al.  Complex patterns in three-dimensional excitable media with advection , 2003 .

[3]  Mark T. Jones,et al.  A Parallel Graph Coloring Heuristic , 1993, SIAM J. Sci. Comput..

[4]  Eva M. Ortigosa,et al.  Spiral waves in three-dimensional excitable media with light-sensitive reaction , 2003 .

[5]  Richard Barrett,et al.  Templates for the Solution of Linear Systems: Building Blocks for Iterative Methods , 1994, Other Titles in Applied Mathematics.

[6]  Achim Basermann Parallel Sparse Matrix Computations in Iterative Solvers on Distributed Memory Machines , 1995, PPSC.

[7]  Luis F. Romero,et al.  Three-dimensional Simulations Of Spiral Waves In Reacting AndDiffusing Media On DSM Computers , 2000 .

[8]  H. Power Applications of high-performance computing in engineering IV , 1995 .

[9]  J. Ramos Dynamics of spiral waves in excitable media with local time-periodic modulation , 2002 .

[10]  Jack Dongarra,et al.  Numerical Linear Algebra for High-Performance Computers , 1998 .

[11]  Victor Eijkhout,et al.  A Matrix Framework for Conjugate Gradient Methods and Some Variants of CG with Less Synchronization Overhead , 1993, PPSC.

[12]  Anne Greenbaum,et al.  Predicting the Behavior of Finite Precision Lanczos and Conjugate Gradient Computations , 2015, SIAM J. Matrix Anal. Appl..

[13]  A. Greenbaum Behavior of slightly perturbed Lanczos and conjugate-gradient recurrences , 1989 .

[14]  Emilio L. Zapata,et al.  Data Distributions for Sparse Matrix Vector Multiplication , 1995, Parallel Comput..

[15]  Cevdet Aykanat,et al.  Vectorization and parallelization of the conjugate gradient algorithm on hypercube-connected vector processors , 1990 .

[16]  Z. Strakos,et al.  On error estimation in the conjugate gradient method and why it works in finite precision computations. , 2002 .

[17]  Ulrich Rüde,et al.  Cache Optimization for Structured and Unstructured Grid Multigrid , 2000 .

[18]  James Demmel,et al.  Parallel numerical linear algebra , 1993, Acta Numerica.

[19]  David E. Keyes,et al.  Proceedings of the Sixth SIAM Conference on Parallel Processing for Scientific Computing, PPSC 1993, Norfolk, Virginia, USA, March 22-24, 1993 , 1993, PPSC.

[20]  Message P Forum,et al.  MPI: A Message-Passing Interface Standard , 1994 .

[21]  A. Winfree The geometry of biological time , 1991 .

[22]  Achim Basermann Conjugate Gradient and Lanczos Methods for Sparse Matrices on Distributed Memory Multiprocessors , 1997, J. Parallel Distributed Comput..

[23]  Jack J. Dongarra,et al.  Solving linear systems on vector and shared memory computers , 1990 .