Efficient Parallel Factorization and Solution of Structured and Unstructured Linear Systems

AbstractThis paper gives improved parallel methods for several exact factorizations of some classes of symmetric positive definite (SPD) matrices. Our factorizations also provide us similarly efficient algorithms for exact computation of the solution of the corresponding linear systems (which need not be SPD), and for finding rank and determinant magnitude. We assume the input matrices have entries that are rational numbers expressed as a ratio of integers with at most a polynomial number of bits β. We assume a parallel random access machine (PRAM) model of parallel computation, with unit cost arithmetic operations, including division, over a finite field Zp, where p is a prime number whose binary representation is linear in the size of the input matrix and is randomly chosen by the algorithm. We require only bit precision O(n(β+logn)), which is the asymptotically optimal bit precision for β⩾logn. Our algorithms are randomized, giving the outputs with high likelihood ⩾1-1/nΩ(1). We compute LU and QR factorizations for dense matrices, and LU factorizations of sparse matrices which are s(n)-separable, reducing the known parallel time bounds for these factorizations from Ω(log3n) to O(log2n), without an increase in processors (matching the best known work bounds of known parallel algorithms with polylog time bounds). Using the same parallel algorithm specialized to structured matrices, we compute LU factorizations for Toeplitz matrices and matrices of bounded displacement rank in time O(log2n) with nloglogn processors, reducing by a nearly linear factor the best previous processor bounds for polylog times (however, these prior works did not generally require unit cost division over a finite field). We use this result to solve in the same bounds: polynomial resultant; and Padé approximants of rational functions; and in a factor O(logn) more time: polynomial greatest common divisors (GCD) and extended GCD; again reducing the best processor bounds by a nearly linear factor

Harnessing the power of GPUs for problems in real algebraic geometry

This thesis presents novel parallel algorithms to leverage the power of GPUs (Graphics Processing Units) for exact computations with polynomials having large integer coefficients. The significance of such computations, especially in real algebraic geometry, is hard to undermine. On massively-parallel architectures such as GPU, the degree of datalevel parallelism exposed by an algorithm is the main performance factor. We attain high efficiency through the use of structured matrix theory to assist the realization of relevant operations on polynomials on the graphics hardware. A detailed complexity analysis, assuming the PRAM model, also confirms that our approach achieves a substantially better parallel complexity in comparison to classical algorithms used for symbolic computations. Aside from the theoretical considerations, a large portion of this work is dedicated to the actual algorithm development and optimization techniques where we pay close attention to the specifics of the graphics hardware. As a byproduct of this work, we have developed high-throughput modular arithmetic which we expect to be useful for other GPU applications, in particular, open-key cryptography. We further discuss the algorithms for the solution of a system of polynomial equations, topology computation of algebraic curves and curve visualization which can profit to the full extent from the GPU acceleration. Extensive benchmarking on a real data demonstrates the superiority of our algorithms over several state-of-the-art approaches available to date. This thesis is written in English.Diese Arbeit beschäftigt sich mit neuen parallelen Algorithmen, die das Leistungspotenzial der Grafik-Prozessoren (GPUs) zur exakten Berechnungen mit ganzzahlige Polynomen nutzen. Solche symbolische Berechnungen sind von großer Bedeutung zur Lösung vieler Probleme aus der reellen algebraischen Geometrie. Für die effziente Implementierung eines Algorithmus auf massiv-parallelen Hardwarearchitekturen, wie z.B. GPU, ist vor allem auf eine hohe Datenparallelität zu achten. Unter Verwendung von Ergebnissen aus der strukturierten Matrix-Theorie konnten wir die entsprechenden Operationen mit Polynomen auf der Grafikkarte leicht übertragen. Außerdem zeigt eine Komplexitätanalyse im PRAM-Rechenmodell, dass die von uns entwickelten Verfahren eine deutlich bessere Komplexität aufweisen als dies für die klassischen Verfahren der Fall ist. Neben dem theoretischen Ergebnis liegt ein weiterer Schwerpunkt dieser Arbeit in der praktischen Implementierung der betrachteten Algorithmen, wobei wir auf der Besonderheiten der Grafikhardware achten. Im Rahmen dieser Arbeit haben wir hocheffiziente modulare Arithmetik entwickelt, von der wir erwarten, dass sie sich für andere GPU Anwendungen, insbesondere der Public-Key-Kryptographie, als nützlich erweisen wird. Darüber hinaus betrachten wir Algorithmen für die Lösung eines Systems von Polynomgleichungen, Topologie Berechnung der algebraischen Kurven und deren Visualisierung welche in vollem Umfang von der GPU-Leistung profitieren können. Zahlreiche Experimente belegen dass wir zur Zeit die beste Verfahren zur Verfügung stellen. Diese Dissertation ist in englischer Sprache verfasst