Evaluating the state of the art of parallel programming systems

This paper describes our plans to evaluate the present state of affairs concerning parallel programming and its systems. Three subprojects are proposed: a survey among programmers and scientists, a comparison of parallel programming systems using a standard set of test programs, and a wiki resource for the parallel programming community - the Parawiki. We would like to invite you to participate and turn these subprojects into true community efforts.

Observations on the Publicity and Usage of Parallel Programming Systems and Languages: A Survey Approach

In this publication, we report on an online survey that was carried out among parallel programmers. More than 250 people worldwide have submitted answers to our questions, and their responses are analyzed here. Although not statistically sound, the data we provide give useful insights about which parallel programming systems and languages are known and in actual use. For instance, the collected data indicate that for our survey group MPI and (to a lesser extent) C are the most widely used parallel programming system and language, respectively.

Genetic Programming meets Model-Driven Development

Genetic programming is known to provide good solutions for many problems like the evolution of network protocols and distributed algorithms. In such cases it is most likely a hardwired module of a design framework that assists the engineer to optimize specific aspects of the system to be developed. It provides its results in a fixed format through an internal interface. In this paper we show how the utility of genetic programming can be increased remarkably by isolating it as a component and integrating it into the model-driven software development process. Our genetic programming framework produces XMI-encoded UML models that can easily be loaded into widely available modeling tools which in turn posses code generation as well as additional analysis and test capabilities. We use the evolution of a distributed election algorithm as an example to illustrate how genetic programming can be combined with model-driven development. This example clearly illustrates the advantages of our approach – the generation of source code in different programming languages.

A User-Centric Perspective on Parallel Programming with Focus on OpenMP

The process of developing software that takes advantage of multiple processors is commonly referred to as parallel programming. For various reasons, this process is much harder than the sequential case. For decades, parallel programming has been a problem for a small niche only: engineers working on parallelizing mostly numerical applications in High Performance Computing. This has changed with the advent of multi-core processors in mainstream computer architectures. Parallel programming in our days becomes a problem for a much larger group of developers. The main objective of this thesis was to find ways to make parallel programming easier for them. Different aims were identified in order to reach the objective: research the state of the art of parallel programming today, improve the education of software developers about the topic, and provide programmers with powerful abstractions to make their work easier. To reach these aims, several key steps were taken. To start with, a survey was conducted among parallel programmers to find out about the state of the art. More than 250 people participated, yielding results about the parallel programming systems and languages in use, as well as about common problems with these systems. Furthermore, a study was conducted in university classes on parallel programming. It resulted in a list of frequently made mistakes that were analyzed and used to create a programmers' checklist to avoid them in the future. For programmers' education, an online resource was setup to collect experiences and knowledge in the field of parallel programming - called the Parawiki. Another key step in this direction was the creation of the Thinking Parallel weblog, where more than 50.000 readers to date have read essays on the topic. For the third aim (powerful abstractions), it was decided to concentrate on one parallel programming system: OpenMP. Its ease of use and high level of abstraction were the most important reasons for this decision. Two different research directions were pursued. The first one resulted in a parallel library called AthenaMP. It contains so-called generic components, derived from design patterns for parallel programming. These include functionality to enhance the locks provided by OpenMP, to perform operations on large amounts of data (data-parallel programming), and to enable the implementation of irregular algorithms using task pools. AthenaMP itself serves a triple role: the components are well-documented and can be used directly in programs, it enables developers to study the source code and learn from it, and it is possible for compiler writers to use it as a testing ground for their OpenMP compilers. The second research direction was targeted at changing the OpenMP specification to make the system more powerful. The main contributions here were a proposal to enable thread-cancellation and a proposal to avoid busy waiting. Both were implemented in a research compiler, shown to be useful in example applications, and proposed to the OpenMP Language Committee.

Evolving Distributed Algorithms with Genetic Programming

Distributed systems are one of the most vital components of the economy. The most prominent example is probably the internet, a constituent element of our knowledge society. During the recent years, the number of novel network types has steadily increased. Amongst others, sensor networks, distributed systems composed of tiny computational devices with scarce resources, have emerged. The further development and heterogeneous connection of such systems imposes new requirements on the software development process. Mobile and wireless networks, for instance, have to organize themselves autonomously and must be able to react to changes in the environment and to failing nodes alike. Researching new approaches for the design of distributed algorithms may lead to methods with which these requirements can be met efficiently. In this thesis, one such method is developed, tested, and discussed in respect of its practical utility. Our new design approach for distributed algorithms is based on Genetic Programming, a member of the family of evolutionary algorithms. Evolutionary algorithms are metaheuristic optimization methods which copy principles from natural evolution. They use a population of solution candidates which they try to refine step by step in order to attain optimal values for predefined objective functions. The synthesis of an algorithm with our approach starts with an analysis step in which the wanted global behavior of the distributed system is specified. From this specification, objective functions are derived which steer a Genetic Programming process where the solution candidates are distributed programs. The objective functions rate how close these programs approximate the goal behavior in multiple randomized network simulations. The evolutionary process step by step selects the most promising solution candidates and modifies and combines them with mutation and crossover operators. This way, a description of the global behavior of a distributed system is translated automatically to programs which, if executed locally on the nodes of the system, exhibit this behavior. In our work, we test six different ways for representing distributed programs, comprising adaptations and extensions of well-known Genetic Programming methods (SGP, eSGP, and LGP), one bio-inspired approach (Fraglets), and two new program representations called Rule-based Genetic Programming (RBGP, eRBGP) designed by us. We breed programs in these representations for three well-known example problems in distributed systems: election algorithms, the distributed mutual exclusion at a critical section, and the distributed computation of the greatest common divisor of a set of numbers. Synthesizing distributed programs the evolutionary way does not necessarily lead to the envisaged results. In a detailed analysis, we discuss the problematic features which make this form of Genetic Programming particularly hard. The two Rule-based Genetic Programming approaches have been developed especially in order to mitigate these difficulties. In our experiments, at least one of them (eRBGP) turned out to be a very efficient approach and in most cases, was superior to the other representations.

Designing a Robust Pitch Angle Controller for a 2‐Motor‐Pitch‐System in a Large Wind Turbine

Für große Windenergieanlagen werden neue Pitchregler wie Einzelblattregler oder Turmdämpfungsregler entwickelt. Während diese neuen Pitchregler die Elemente der Windenergieanlagen entlasten, wird das Pitchantriebssystem stärker belastet. Die Pitchantriebe müssen weitaus häufiger bei höherer Amplitude arbeiten. Um die neuen Pitchregler nutzen zu können, muss zunächst das Problem der Materialermüdung der Pitchantriebssysteme gelöst werden. Das Getriebespiel in Getrieben und zwischen Ritzeln und dem Zahnkranz erhöht die Materialermüdung in den Pitchantriebssystemen. In dieser Studie werden als Lösung zwei Pitchantriebe pro Blatt vorgeschlagen. Die beiden Pitchantriebe erzeugen eine Spannung auf dem Pitchantriebssystem und kompensieren das Getriebespiel. Drehmomentspitzen, die eine Materialermüdung verursachen, treten bei diesem System mit zwei Pitchmotoren nicht mehr auf. Ein Reglerausgang wird via Drehmomentverteiler auf die beiden Pitchantriebe übertragen. Es werden mehrere Methoden verglichen und der leistungsfähigste Drehmomentverteiler ausgewählt. Während die Pitchantriebe in Bewegung sind, ändert sich die Spannung auf den Getrieben. Die neuen Pitchregler verstellen den Pitchwinkel in einer sinusförmigen Welle. Der Profilgenerator, der derzeit als Pitchwinkelregler verwendet wird, kann eine Phasenverzögerung im sinusförmigen Pitchwinkel verursachen. Zusätzlich erzeugen große Windenergieanlagen eine hohe Last, die sich störend auf die Pitchbewegung auswirkt. Änderungen der viskosen Reibung und Nichtlinearität der Gleitreibung bzw. Coulombsche Reibung des Pitchregelsystems erschweren zudem die Entwicklung eines Pitchwinkelreglers. Es werden zwei robuste Regler (H∞ und μ–synthesis ) vorgestellt und mit zwei herkömmlichen Reglern (PD und Kaskadenregler) verglichen. Zur Erprobung des Pitchantriebssystems und des Pitchwinkelreglers wird eine Prüfanordnung verwendet. Da der Kranz nicht mit einem Positionssensor ausgestattet ist, wird ein Überwachungselement entwickelt, das die Kranzposition meldet. Neben den beiden Pitchantrieben sind zwei Lastmotoren mit dem Kranz verbunden. Über die beiden Lastmotoren wird das Drehmoment um die Pitchachse einer Windenergieanlage simuliert. Das Drehmoment um die Pitchachse setzt sich zusammen aus Schwerkraft, aerodynamischer Kraft, zentrifugaler Belastung, Reibung aufgrund des Kippmoments und der Beschleunigung bzw. Verzögerung des Rotorblatts. Das Blatt wird als Zweimassenschwinger modelliert. Große Windenergieanlagen und neue Pitchregler für die Anlagen erfordern ein neues Pitchantriebssystem. Als Hardware-Lösung bieten sich zwei Pitchantriebe an mit einem robusten Regler als Software.