Oil and Water? High Performance Garbage Collection in Java with MMTk

Increasingly popular languages such as Java and C # require efficient garbage collection. This paper presents the design, implementation, and evaluation of MMTk, a Memory Management Toolkit for and in Java. MMTk is an efficient, composable, extensible, and portable framework for building garbage collectors. MMTk uses design patterns and compiler cooperation to combine modularity and efficiency. The resulting system is more robust, easier to maintain, and has fewer defects than monolithic collectors. Experimental comparisons with monolithic Java and C implementations reveal MMTk has significant performance advantages as well. Performance critical system software typically uses monolithic C at the expense of flexibility. Our results refute common wisdom that only this approach attains efficiency, and suggest that performance critical software can embrace modular design and high-level languages.

Decrypting The Java Gene Pool: Predicting Objects' Lifetimes with Micro-patterns

Pretenuring long-lived and immortal objects into infrequently or never collected regions reduces garbage collection costs significantly. However, extant approaches either require computationally expensive, application-specific, off-line profiling, or consider only allocation sites common to all programs, i.e. invoked by the virtual machine rather than application programs. In contrast, we show how a simple program analysis, combined with an object lifetime knowledge bank, can be exploited to match both runtime system and application program structure with object lifetimes. The complexity of the analysis is linear in the size of the program, so need not be run ahead of time. We obtain performance gains between 6-77% in GC time against a generational copying collector for several SPEC jvm98 programs

Testing iPad in the field: use of a relational database in garbological research

Recording thousands of entries during field research poses a challenge to any field researcher. Contemporary handheld computers offer affordable solutions, which can resolve this challenge. In this paper, we test the iPad tablet computer and FileMaker Go database to conduct garbological research carried out in West Bohemia (Czech Republic). Garbological research based on the collection of data about human waste requires not only efficient tools for recording a vast number of individual garbage items on the spot but also integration of multiple analytical levels in a database. Our research was aimed at household waste to illuminate consumption patterns and mobility of humans and things in contemporary Central European settings. The iPad was used to collect textual and visual data and integrate these in a relational database. We describe our methodology and experience with this kind of technology. The iPad and FileMaker Go proved to be well suited to challenging field conditions in the landfill, data collection was efficient and reliable, the database was flexible because its basic features could be modified in the field, and one could even examine preliminary trends in the data using charts in FileMaker Go. The proposed hardware and software is less efficient for the collection of precise spatial data, preparation of accurate drawings, and for projects in remote areas without good access to an electrical grid

Heap Abstractions for Static Analysis

Heap data is potentially unbounded and seemingly arbitrary. As a consequence, unlike stack and static memory, heap memory cannot be abstracted directly in terms of a fixed set of source variable names appearing in the program being analysed. This makes it an interesting topic of study and there is an abundance of literature employing heap abstractions. Although most studies have addressed similar concerns, their formulations and formalisms often seem dissimilar and some times even unrelated. Thus, the insights gained in one description of heap abstraction may not directly carry over to some other description. This survey is a result of our quest for a unifying theme in the existing descriptions of heap abstractions. In particular, our interest lies in the abstractions and not in the algorithms that construct them. In our search of a unified theme, we view a heap abstraction as consisting of two features: a heap model to represent the heap memory and a summarization technique for bounding the heap representation. We classify the models as storeless, store based, and hybrid. We describe various summarization techniques based on k-limiting, allocation sites, patterns, variables, other generic instrumentation predicates, and higher-order logics. This approach allows us to compare the insights of a large number of seemingly dissimilar heap abstractions and also paves way for creating new abstractions by mix-and-match of models and summarization techniques.Comment: 49 pages, 20 figure