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Author Hicks, Michael W. ♦ Moore, Jonathan T. ♦ Nettles, Scott M.
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Computer programming, programs & data
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.
Description Affiliation: Computer and Information Science Department, University of Pennsylvania, Philadelphia, PA (Hicks, Michael W.; Moore, Jonathan T.; Nettles, Scott M.)
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 1983-05-01
Publisher Place New York
Journal ACM SIGPLAN Notices (SIGP)
Volume Number 32
Issue Number 8
Page Count 14
Starting Page 292
Ending Page 305


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Source: ACM Digital Library