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The paper considers the 'ijk forms' of LU and Cholesky factorization on certain parallel computers. This extends an earlier analysis for vector computers. Attention is restricted to local memory systems with processors that may or may not have vector capability. Special attention is given to bus architectures but qualitative analyses are given for other interconnection systems.
Up to today, preconditioning methods on massively parallel systems have faced a major difficulty. The most successful preconditioning methods in terms of accelerating the convergence of the iterative solver such as incomplete LU factorizations are notoriously difficult to implement on parallel machines for two reasons: (1) the actual computation of the preconditioner is not very floating-point intensive, but requires a large amount of unstructured communication, and (2) the application of the preconditioning matrix in the iteration phase (i.e. triangular solves) are difficult to parallelize because of the recursive nature of the computation. Here we present a new approach to preconditioning for very large, sparse, unsymmetric, linear systems, which avoids both difficulties. We explicitly compute an approximate inverse to our original matrix. This new preconditioning matrix can be applied most efficiently for iterative methods on massively parallel machines, since the preconditioning phase involves only a matrix-vector multiplication, with possibly a dense matrix. Furthermore the actual computation of the preconditioning matrix has natural parallelism. For a problem of size n, the preconditioning matrix can be computed by solving n independent small least squares problems. The algorithm and its implementation on the Connection Machine CM-5 are discussed in detail and supported by extensive timings obtained from real problem data.
The compressible Navier-Stokes equations are solved for a variety of two-dimensional inviscid and viscous problems by preconditioned conjugate gradient-like algorithms. Roe's flux difference splitting technique is used to discretize the inviscid fluxes. The viscous terms are discretized by using central differences. An algebraic turbulence model is also incorporated. The system of linear equations which arises out of the linearization of a fully implicit scheme is solved iteratively by the well known methods of GMRES (Generalized Minimum Residual technique) and Chebyschev iteration. Incomplete LU factorization and block diagonal factorization are used as preconditioners. The resulting algorithm is competitive with the best current schemes, but has wide applications in parallel computing and unstructured mesh computations.
This paper considers an algebraic preconditioning algorithm for hyperbolic-elliptic fluid flow problems. The algorithm is based on a parallel non-overlapping Schur complement domain-decomposition technique for triangulated domains. In the Schur complement technique, the triangulation is first partitioned into a number of non-overlapping subdomains and interfaces. This suggests a reordering of triangulation vertices which separates subdomain and interface solution unknowns. The reordering induces a natural 2 x 2 block partitioning of the discretization matrix. Exact LU factorization of this block system yields a Schur complement matrix which couples subdomains and the interface together. The remaining sections of this paper present a family of approximate techniques for both constructing and applying the Schur complement as a domain-decomposition preconditioner. The approximate Schur complement serves as an algebraic coarse space operator, thus avoiding the known difficulties associated with the direct formation of a coarse space discretization. In developing Schur complement approximations, particular attention has been given to improving sequential and parallel efficiency of implementations without significantly degrading the quality of the preconditioner. A computer code based on these developments has been tested on the IBM SP2 using MPI message passing protocol. A number of 2-D calculations are presented for both scalar advection-diffusion equations as well as the Euler equations governing compressible fluid flow to demonstrate performance of the preconditioning algorithm.
In the last three years extensive performance data have been reported for parallel machines both based on the NAS Parallel Benchmarks, and on LINPACK. In this study we have used the reported benchmark results and performed a number of statistical experiments using factor, cluster, and regression analyses. In addition to the performance results of LINPACK and the eight NAS parallel benchmarks, we have also included peak performance of the machine, and the LINPACK n and n(sub 1/2) values. Some of the results and observations can be summarized as follows: 1) All benchmarks are strongly correlated with peak performance. 2) LINPACK and EP have each a unique signature. 3) The remaining NPB can grouped into three groups as follows: (CG and IS), (LU and SP), and (MG, FT, and BT). Hence three (or four with EP) benchmarks are sufficient to characterize the overall NPB performance. Our poster presentation will follow a standard poster format, and will present the data of our statistical analysis in detail.
The critical ionization velocity (CIV) mechanism in a finite size cloud is studied with a series of electrostatic particle-in-cell simulations. It is observed that an initial seed ionization, produced by non-CIV mechanisms, generates a cross-field ion beam which excites a modified beam-plasma instability (MBPI) with frequency in the range of the lower hybrid frequency. The excited waves accelerate electrons along the magnetic field up to the ion drift energy that exceeds the ionization energy of the neutral atoms. The heated electrons in turn enhance the ion beam by electron-neutral impact ionization, which establishes a positive feedback loop in maintaining the CIV process. It is also found that the efficiency of the CIV mechanism depends on the finite size of the gas cloud in the following ways: (1) Along the ambient magnetic field the finite size of the cloud, L (sub parallel), restricts the growth of the fastest growing mode, with a wavelength lambda (sub m parallel), of the MBPI. The parallel electron heating at wave saturation scales approximately as (L (sub parallel)/lambda (sub m parallel)) (exp 1/2); (2) Momentum coupling between the cloud and the ambient plasma via the Alfven waves occurs as a result of the finite size of the cloud in the direction perpendicular to both the ambient magnetic field and the neutral drift. This reduces exponentially with time the relative drift between the ambient plasma and the neutrals. The timescale is inversely proportional to the Alfven velocity. (3) The transvers e charge separation field across the cloud was found to result in the modulation of the beam velocity which reduces the parallel heating of electrons and increases the transverse acceleration of electrons. (4) Some energetic electrons are lost from the cloud along the magnetic field at a rate characterized by the acoustic velocity, instead of the electron thermal velocity. The loss of energetic electrons from the cloud seems to be larger in the direction of plasma drift relative to the neutrals, where the loss rate is characterized by the neutral drift velocity. It is also shown that a factor of 4 increase in the ambient plasma density, increases the CIV ionization yield by almost 2 orders of magnitude at the end of a typical run. It is concluded that a larger ambient plasma density can result in a larger CIV yield because of (1) larger seed ion production by non-CIV mechanisms, (2) smaller Alfven velocity and hence weak momentum coupling, and (3) smaller ratio of the ion beam density to the ambient ion density, and therefore a weaker modulation of the beam velocity. The simulation results are used to interpret various chemical release experiments in space.