Beltway: Getting Around Garbage Collection Gridlock

We present the design and implementation of a new garbage collection framework that significantly generalizes existing copying collectors. The Beltway framework exploits and separates object age and incrementality. It groups objects in one or more increments on queues called belts, collects belts independently, and collects increments on a belt in first-in-first-out order. We show that Beltway configurations, selected by command line options, act and perform the same as semi-space, generational, and older-first collectors, and encompass all previous copying collectors of which we are aware. The increasing reliance on garbage collected languages such as Java requires that the collector perform well. We show that the generality of Beltway enables us to design and implement new collectors that are robust to variations in heap size and improve total execution time over the best generational copying collectors of which we are aware by up to 40%, and on average by 5 to 10%, for small to moderate heap sizes. New garbage collection algorithms are rare, and yet we define not just one, but a new family of collectors that subsumes previous work. This generality enables us to explore a larger design space and build better collectors

Muistin siivous

Tutkielmassa esitellään roskan käsite tietojenkäsittelytieteessä, roskienkeruun keskeiset käsitteet ja perusmenetelmät muunnelmineen sekä nykyaikaiset tehokkaat algoritmit. Keskipisteenä ovat kuitenkin muistinhallintatutkimuksen 2000-luvun saavutukset, tutkimusaiheet ja tutkimusvälineet. Näitä hyödyntää tutkielmassa esiteltävä uusi CBRC-roskienkeruualgoritmi. Lisäksi katsastetaan ohjelmoijan vastuu automaattisessa muistinhallinnassa sekä ohjelmoinnissa käytettävissä olevat roskienkeruutietoiset välineet eräissä ohjelmointikielissä ja –ympäristöissä (Java, .Net, C++). Avainsanat ja -sanonnat: roskienkeruu, muistinsiivous, muistinhallinta, algoritmit, ohjelmointikielet CR-luokat: D 3.4, D.4.2, D.3.

High Performance Reference Counting and Conservative Garbage Collection

Garbage collection is an integral part of modern programming languages. It automatically reclaims memory occupied by objects that are no longer in use. Garbage collection began in 1960 with two algorithmic branches — tracing and reference counting. Tracing identifies live objects by performing a transitive closure over the object graph starting with the stacks, registers, and global variables as roots. Objects not reached by the trace are implicitly dead, so the collector reclaims them. In contrast, reference counting explicitly identifies dead objects by counting the number of incoming references to each object. When an object’s count goes to zero, it is unreachable and the collector may reclaim it. Garbage collectors require knowledge of every reference to each object, whether the reference is from another object or from within the runtime. The runtime provides this knowledge either by continuously keeping track of every change to each reference or by periodically enumerating all references. The collector implementation faces two broad choices — exact and conservative. In exact garbage collection, the compiler and runtime system precisely identify all references held within the runtime including those held within stacks, registers, and objects. To exactly identify references, the runtime must introspect these references during execution, which requires support from the compiler and significant engineering effort. On the contrary, conservative garbage collection does not require introspection of these references, but instead treats each value ambiguously as a potential reference. Highly engineered, high performance systems conventionally use tracing and exact garbage collection. However, other well-established but less performant systems use either reference counting or conservative garbage collection. Reference counting has some advantages over tracing such as: a) it is easier implement, b) it reclaims memory immediately, and c) it has a local scope of operation. Conservative garbage collection is easier to implement compared to exact garbage collection because it does not require compiler cooperation. Because of these advantages, both reference counting and conservative garbage collection are widely used in practice. Because both suffer significant performance overheads, they are generally not used in performance critical settings. This dissertation carefully examines reference counting and conservative garbage collection to understand their behavior and improve their performance. My thesis is that reference counting and conservative garbage collection can perform as well or better than the best performing garbage collectors. The key contributions of my thesis are: 1) An in-depth analysis of the key design choices for reference counting. 2) Novel optimizations guided by that analysis that significantly improve reference counting performance and make it competitive with a well tuned tracing garbage collector. 3) A new collector, RCImmix, that replaces the traditional free-list heap organization of reference counting with a line and block heap structure, which improves locality, and adds copying to mitigate fragmentation. The result is a collector that outperforms a highly tuned production generational collector. 4) A conservative garbage collector based on RCImmix that matches the performance of a highly tuned production generational collector. Reference counting and conservative garbage collection have lived under the shadow of tracing and exact garbage collection for a long time. My thesis focuses on bringing these somewhat neglected branches of garbage collection back to life in a high performance setting and leads to two very surprising results: 1) a new garbage collector based on reference counting that outperforms a highly tuned production generational tracing collector, and 2) a variant that delivers high performance conservative garbage collection

Programming constraint services

This thesis presents design, application, implementation, and evaluation of computation spaces as abstractions for programming constraint services at a high level. Spaces are seamlessly integrated into a concurrent programming language and make constraintbased computations compatible with concurrency through encapsulation. Spaces are applied to search and combinators as essential constraint services. State-of-the-art and new search engines such as visual interactive search and parallel search are covered. Search is expressive and concurrency-compatible by using copying rather than trailing. Search is space and time efficient by using recomputation. Composable combinators, also known as deep-guard combinators, stress the control facilities and concurrency integration of spaces. The implementation of spaces comes as an orthogonal extension to the implementation of the underlying programming language. The resulting implementation is shown to be competitive with existing constraint programming systems.Diese Dissertation beschreibt Entwurf, Verwendung, Implementierung und Evaluierung von Computation Spaces für die Programmierung von Constraintdiensten. Spaces werden in eine nebenläufige Programmiersprache integriert. Sie fungieren als Kapseln für Berechnungen mit Constraints. Dadurch wird die Kompatibilität zu nebenläufigen Berechnungen gewährleistet. Suche und Kombinatoren sind zentrale Constraintdienste, die mit Spaces programmiert werden. Es werden sowohl übliche, als auch vollkommen neue Suchmaschinen, wie zum Beispiel interaktive Suche und parallele Suche, vorgestellt. Durch Kopieren wird Suche ausdrucksstark und kompatibel mit Nebenläufigkeit. Durch Wiederberechnung wird Suche effizient hinsichtlich Speicherbedarf und Laufzeit. Kombinatoren, die ineinander geschachtelt werden können (so genannte deep-guard Kombinatoren), verdeutlichen die Kontrollmöglichkeiten von Spaces. Die Implementierung von Spaces erfolgt als orthogonale Erweiterung einer Implementierung für die zugrundeliegende Programmiersprache. Das Ergebnis ist konkurrenzfähig zu existierenden Constraintprogrammiersystemen

Computer-Assisted Lighting Design and Control

This dissertation shows that computer-based lighting control systems can support the lighting design process considerably better than traditional consoles. It describes the Lula Project, a new software package for lighting design and control, that implements this level of support. Lula's focus is on the conceptual ideas behind a lighting design rather than the concrete lighting fixtures used to put it on stage. Among the innovative aspects of the system are its model for designing static lighting looks and its subsystem for programmable continuous animated lighting. Lula's application design is centered around the idea of componential lighting design that allows the user to express a lighting design as a hierarchy of components. Lula is a result of the rigorous application of high-level software engineering techniques and implementation technology from the general realm of functional programming. The high-level structure of the application rests upon stratified design, algebraic modelling, and domain-specific languages. Among the implementation techniques instrumental to Lula are automatic memory management, higher-order programming, functional data structures, data-directed programming, parametric inheritance, and concurrent programming.Computer-basierte Systeme für Beleuchtungssteuerung sind in der Lage, den Lichtdesigner weitaus besser zu unterstützen als es derzeit marktübliche Steuerkonsolen tun. Das Thema dieser Dissertation ist ein solches System, das Projekt Lula. Lula ist eine neue Software für Lichtregie und Beleuchtungssteuerung, welche die Modellierung der konzeptuellen Elemente eines Lichtdesigns ermöglicht, unabhängig von der konkreten Realisierung auf der Bühne. Unter den innovativen Aspekten des Systems ist das Modell für den Entwurf statischer Beleuchtungsszenen sowie das Subsystem für programmierbare, stetig animierte Beleuchtung. Das übergeordnete Prinzip bei Lula ist komponentenbasierte Lichtregie, die es dem Benutzer erlaubt, ein Lichtdesign als eine Hierarchie von Komponenten auszudrücken. Lula ist das Resultat konsequenter Anwendung von Entwurfs- und Implementierungs-Techniken aus dem Bereich der funktionalen Programmierung. Die High-Level-Struktur des Systems baut auf stratifiziertes Design, algebraische Modellierung und anwendungsspezifische Programmiersprachen. Unter den Implementationstechniken, die entscheidend bei der Entwicklung von Lula waren, befinden sich automatische Speicherverwaltung, Higher-Order-Programmierung, funktionale Datenstrukturen, datengesteuerte Programmierung, parametrische Vererbung und nebenläufige Programmierung