The measured cost of copying garbage collection mechanisms

Abstract

We examine the costs and benefits of a variety of copying garbage collection (GC) mechanisms across multiple architectures and programming languages. Our study covers both low-level object representation and copying issues as well as the mechanisms needed to support more advanced techniques such as generational collection, large object spaces, and type segregated areas.Our experiments are made possible by a novel performance analysis tool, Oscar. Oscar allows us to capture snapshots of programming language heaps that may then be used to replay garbage collections. The replay program is self-contained and written in C, which makes it easy to port to other architectures and to analyze with standard performance analysis tools. Furthermore, it is possible to study additional programming languages simply by instrumenting existing implementations to capture heap snapshots.In general, we found that careful implementation of GC mechanisms can have a significant benefit. For a simple collector, we measured improvements of as much as 95%. We then found that while the addition of advanced features can have a sizeable overhead (up to 15%), the net benefit is quite positive, resulting in additional gains of up to 42%. We also found that results varied depending upon the platform and language. Machine characteristics such as cache arrangements, instruction set (RISC/CISC), and register pool were important. For different languages, average object size seemed to be most important.The results of our experiments demonstrate the usefulness of a tool like Oscar for studying GC performance. Without much overhead, we can easily identify areas where programming language implementors could collaborate with GC implementors to improve GC performance.