Thesis (M. Eng.)--University of Louisville, 2004. / Department of Computer Engineering and Computer Science. Vita. "May 2004." Includes bibliographical references (leaves 61-63).

Thesis (Ph. D.)--Cornell University, 1981. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 311-345).

Thesis--Leiden. / Includes index. "Bibliography since 1908": p. [172]-179.

Thesis (doctoral)--Rijksuniversiteit te Leiden, 1861.

Thesis (M.S. in computer science)--Washington State University, May 2010. / Title from PDF title page (viewed on July 28, 2010). "School of Engineering and Computer Science." Includes bibliographical references (p. 65-67).

Thesis (Ph. D.)--University of Washington, 2007. / Vita. Includes bibliographical references (p. 511-533).

Proefschrift Universiteit Maastricht. / Met lit. opg. - Met samenvatting in het Nederlands.

Texte remanié de: Diss.--Philosophische Fakultät--Bonn--Universität, 1993. / Bibliogr. p. 205-210.

Title Garbage Collection in Software Performance Engineering Author Peter Libič peter.libic@d3s.mff.cuni.cz Advisor doc. Ing. Petr Tůma, Dr. petr.tuma@d3s.mff.cuni.cz Department Department of Distributed and Dependable Systems Faculty of Mathematics and Physics Charles University Malostranské nám. 25, 118 00 Prague, Czech Republic Abstract The increasing popularity of languages with automatic memory management makes the garbage collector (GC) performance key to effective application execution. Unfortunately, performance behavior of contemporary GC is not well understood by the application developers and often ignored by the per- formance model designers. In this thesis, we (1) evaluate nature of GC overhead with respect to its effect on accuracy of performance models. We assess the possibility to model GC overhead as a black-box and identify workload characteristics that contribute to GC performance. Then we (2) design an analytical model of one-generation collector and a simulation model of both one-generation and two-generation collectors. These models rely on application characteristics. We evaluate the accuracy of such models and perform an analysis of their sensitivity to the inputs. Using the model we expose the gap between under- standing the GC overhead based on knowing the algorithm...

Embedded systems can be found everywhere in our daily lives. Due to the great variety of embedded devices, the platform independent Java language provides a good solution for embedded system development. Java virtual machine (JVM) is the most critical component of all kinds of Java platforms. Hence, it is extremely important to study the special design of JVM for embedded systems. The key challenges of designing a successful JVM for embedded systems are energy efficiency, time predictability and performance, which are investigated in this dissertation, respectively. We first study the energy issue of JVM on embedded systems. With a cycle-accurate simulator, we study each stage of Java execution separately to test the effects of different configurations in both software and hardware. After that, an alternative Adaptive Optimization System (AOS) model is introduced, which estimated the cost/benefit using energy data instead of running time. We tuned the parameters of this model to study how to improve the dynamic compilation and optimization in Jikes RVM in terms of energy consumption. In order to further reduce the energy dissipation of JVM on embedded systems, we study adaptive drowsy cache control for Java applications, where JVM can be used to make better decision on drowsy cache control. We explore the impact of different phases of Java applications on the timing behavior of cache usage. Then we propose several techniques to adaptively control drowsy cache to reduce energy consumption with minimal impact on performance. It is observed that traditional Java code generation and instruction fetch path are not efficient. So we study three hardware-based code caching strategies, which attempt to write and read the dynamically generated Java code faster and more energy-efficiently. Time predictability is another key challenge for JVM on embedded systems. So we exploit multicore computing to reduce the timing unpredictability caused by dynamic compilation and adaptive optimization. Our goal is to retain high performance comparable to that of traditional dynamic compilation and, at the same time, obtain better time predictability for JVM. We study pre-compilation techniques to utilize another core more efficiently. Furthermore, we develop Pre-optimization on Another Core (PoAC) scheme to replace AOS in Jikes JVM, which is very sensitive to execution time variation and impacts time predictability greatly. Finally, we propose two new approaches that automatically parallelizes Java programs at run-time, in order to meet the performance challenge of JVM on embedded systems. These approaches rely on run-time trace information collected during program execution, and dynamically recompiles Java byte code that can be executed in parallel. One approach utilizes trace information to improve traditional loop parallelization, and the other parallelizes traces instead of loop iterations.