HONEI: A collection of libraries for numerical computations targeting multiple processor architectures.

We present HONEI, an open-source collection of libraries offering a hardware oriented approach to numerical calculations. HONEI abstracts the hardware, and applications written on top of HONEI can be executed on a wide range of computer architectures such as CPUs, GPUs and the Cell processor. We demonstrate the flexibility and performance of our approach with two test applications, a Finite Element multigrid solver for the Poisson problem and a robust and fast simulation of shallow water waves. By linking against HONEI's libraries, we achieve a two-fold speedup over straight forward C++ code using HONEI's SSE backend, and additional 3--4 and 4--16 times faster execution on the Cell and a GPU. A second important aspect of our approach is that the full performance capabilities of the hardware under consideration can be exploited by adding optimised application-specific operations to the HONEI libraries. HONEI provides all necessary infrastructure for development and evaluation of such kernels, significantly simplifying their development

GPU-Accelerated Asynchronous Error Correction for Mixed Precision Iterative Refinement

In hardware-aware high performance computing, block-asynchronous iteration and mixed precision iterative refinement are two techniques that may be used to leverage the computing power of SIMD accelerators like GPUs in the iterative solution of linear equation systems. although they use a very different approach for this purpose, they share the basic idea of compensating the convergence properties of an inferior numerical algorithm by a more efficient usage of the provided computing power. In this paper, we analyze the potential of combining both techniques. Therefore, we derive a mixed precision iterative refinement algorithm using a block-asynchronous iteration as an error correction solver, and compare its performance with a pure implementation of a block-asynchronous iteration and an iterative refinement method using double precision for the error correction solver. For matrices from the University of Florida Matrix collection, we report the convergence behaviour and provide the total solver runtime using different GPU architectures

Parallel GPU implementations of numerical methods for fluid dynamics

This article presents the application of parallel computing techniques using Graphic Processing Unit (GPU) in order to improve the computational efficiency of numerical methods applied to uid dynamics problems. In the last ten years, GPUs have emerged as a major paradigm for solving complex problems using parallel computing techniques. Fluid dynamics problems usually requires large execution times to perform simulations for realistic scenarios. In this work, two numerical models for fluid dynamics are presented, and parallel implementations on GPU for the Strongly Implicit Procedure and the Cyclic Reduction methods for solving linear systems are introduced. The experimental evaluation of the proposed methods demonstrates that a significant reduction on the computing times can be attained when solving linear systems with representative dimensions, and preliminary results show that the efficiency gains also propagate to the numerical models for fluid dynamics.Sociedad Argentina de Informática e Investigación Operativ

Fast and accurate finite-element multigrid solvers for PDE simulations on GPU clusters

Der wichtigste Beitrag dieser Dissertation ist es aufzuzeigen, dass Grafikprozessoren (GPUs) als Repräsentanten der Entwicklung hin zu Vielkern-Architekturen sehr gut geeignet sind zur schnellen und genauen Lösung großer, dünn besetzter linearer Gleichungssysteme, insbesondere mit parallelen Mehrgittermethoden auf heterogenen Rechenclustern. Solche Systeme treten bspw. bei der Diskretisierung (elliptischer) partieller Differentialgleichungen mittels finiter Elemente auf. Wir demonstrieren Beschleunigungsfaktoren von mindestens einer Größenordnung gegenüber konventionellen, hochoptimierten CPU-Implementierungen, ohne Verlust von Genauigkeit und Funktionsumfang. Im Detail liefert diese Dissertation die folgenden Beiträge: Berechnungen in einfach genauer Fließkommadarstellung können für die hier betrachteten Problemklassen nicht ausreichen. Wir greifen die Methode gemischt genauer iterativer Verfeinerung (Nachiteration) wieder auf, um nicht nur die Genauigkeit von berechneten Lösungen zu verbessern, sondern vielmehr die Effizienz des Lösungsprozesses als ganzes zu steigern. Sowohl auf CPUs als auch auf GPUs demonstrieren wir eine deutliche Leistungssteigerung ohne Genauigkeitsverlust im Vergleich zur Berechnung in höherer Fliesskomma-Genauigkeit. Wir präsentieren effiziente Parallelisierungstechniken für Mehrgitter-Löser auf Grafik-Hardware, insbesondere für numerisch starke Glätter und Vorkonditionierer, die für stark anisotrope Gitter und Operatoren geeignet sind. Ein Beispiel ist die Entwicklung einer effizienten Reformulierung des Verfahrens der zyklischen Reduktion für die Lösung tridiagonaler Gleichungssysteme. Im Hinblick auf Hardware-orientierte Numerik analysieren wir sorgfältig den Kompromiss zwischen numerischer und Laufzeit-Effizienz für inexakte Parallelisierungstechniken, die einige der inhärent sequentiellen Charakteristiken solcher starker Glätter zugunsten besserer Parallelisierungseigenschaften entkoppeln. Die Reimplementierung großer, etablierter Softwarepakete zur Anpassung auf neue Hardwareplattformen ist oft inakzeptabel teuer. Wir entwickeln einen "minimalinvasiven" Zugang zur Integration von Co-Prozessoren wie GPUs in FEAST, einem exemplarischen finite Elemente Diskretisierungs- und Löserpaket. Der Hauptvorteil unserer Technik ist, dass Applikationen, die auf FEAST aufsetzen, nicht geändert werden müssen um von der Beschleunigung durch solche Co-Prozessoren zu profitieren. Wir evaluieren unseren Zugang auf großen GPU-beschleunigten Rechenclustern für klassische Benchmarkprobleme aus der linearisierten Elastizität und der Simulation stationärer laminarer Strömungsvorgänge, und beobachten gute Beschleunigungsfaktoren und gute schwache Skalierbarkeit. Die maximal erreichbare Beschleunigung wird zudem analysiert und theoretisch modelliert, um bspw. Vorhersagen treffen zu können. Weiterhin fassen wir die historische Entwicklung des Forschungsgebiets "wissenschaftliches Rechnen auf Grafikhardware" seit 2001/2002 zusammen, d.h. die Entwicklung von GPGPU als obskures Nischenthema hin zum fachübergreifenden Einsatz heute. Die Darstellung umfasst gleichermaßen die Hardware und das Programmiermodell und beinhaltet eine ausgiebige Bibliografie von Veröffentlichungen im Bereich der Simulation von PDE-Problemen auf GPUs.The main contribution of this thesis is to demonstrate that graphics processors (GPUs) as representatives of emerging many-core architectures are very well-suited for the fast and accurate solution of large sparse linear systems of equations, using parallel multigrid methods on heterogeneous compute clusters. Such systems arise for instance in the discretisation of (elliptic) partial differential equations with finite elements. We report on at least one order of magnitude speedup over highly-tuned conventional CPU implementations, without sacrificing neither accuracy nor functionality. In more detail, this thesis includes the following contributions: Single precision floating point computations may be insufficient for the class of problems considered in this thesis. We revisit mixed precision iterative refinement techniques to not only increase the accuracy of computed results, but also to increase the efficiency of the solution process. Both on CPUs and on GPUs, we demonstrate a significant performance improvement without loss of accuracy compared to computing in high precision only. We present efficient parallelisation techniques for multigrid solvers on graphics hardware, in particular for numerically strong smoothers and preconditioners that are suitable for highly anisotropic grids and operators. For instance, an efficient formulation of the cyclic reduction algorithm to solve tridiagonal systems is developed. In view of hardware-oriented numerics, we carefully analyse the trade-off between numerical and runtime performance for inexact parallelisation techniques that decouple some of the inherently sequential characteristics of strong smoothing operators. For large-scale established software frameworks, the re-implementation tailored to novel hardware platforms is often prohibitively expensive. We develop a 'minimally invasive' approach to integrate support for co-processor hardware like GPUs into FEAST, a finite element discretisation and solver toolbox. Our technique has the major advantage that applications built on top of the toolbox do not have to be changed at all to benefit from co-processor acceleration. The approach is evaluated for benchmark problems in linearised elasticity and stationary laminar flow computed on large-scale GPU-enhanced clusters. Good speedup factors and near-ideal weak scalability are observed. The achievable speedup is analysed and a theoretical speedup model is presented. Finally, we provide a historical overview of scientific computing on graphics hardware since the early beginnings in 2001/2002, when GPGPU was an obscure research topic pursued by few, to the widespread adoption nowadays. We discuss the evolution of the hardware and the programming model, and provide a comprehensive bibliography of publications related to PDE simulations on GPUs

GPU-Accelerated Asynchronous Error Correction for Mixed Precision Iterative Refinement

In hardware-aware high performance computing, block-asynchronous iteration and mixed precision iterative refinement are two techniques that may be used to leverage the computing power of SIMD accelerators like GPUs in the iterative solution of linear equation systems. although they use a very different approach for this purpose, they share the basic idea of compensating the convergence properties of an inferior numerical algorithm by a more efficient usage of the provided computing power. In this paper, we analyze the potential of combining both techniques. Therefore, we derive a mixed precision iterative refinement algorithm using a block-asynchronous iteration as an error correction solver, and compare its performance with a pure implementation of a block-asynchronous iteration and an iterative refinement method using double precision for the error correction solver. For matrices from the University of Florida Matrix collection, we report the convergence behaviour and provide the total solver runtime using different GPU architectures