Comparative Performance Evaluation of Garbage Collection Algorithms
Abstract: This thesis shows that object-level, trace-driven simulation can facilitate evaluation of language runtime systems and reaches new conclusions about the relative performance of important garbage collection algorithms. In particular, I reach the unexpected conclusion that mark-and-sweep garbage collection, when augmented with generations, shows comparable CPU performance and much better reference locality than the more widely used copying algorithms.
In the past, evaluation of garbage collection algorithms has been limited by the high cost of implementing the algorithms. Substantially different algorithms have rarely been compared in a systematic way. With the availability of high-performance, low-cost workstations, trace-driven performance evaluation of these algorithms is now economical. This thesis describes MARS, a runtime system simulator that is driven by operations on program objects, and not memory addresses. MARS has been attached to a commercial Common Lisp system and eight large Lisp applications are used in the thesis as test programs.
To illustrate the advantages of the object-level tracing technique used by MARS, this thesis compares the relative performance of stop-and-copy, incremental, and mark-and-sweep collection algorithms, all organized with multiple generations. The comparative evaluation is based on several metrics: CPU overhead, reference locality, and interactive availability. Mark-and-sweep collection shows slightly higher CPU overhead than stop-and-copy collection (5%), but requires significantly less physical memory to achieve the same page fault rate (30-40%). Incremental collection has very good interactive availability, but implementing the read barrier on stock hardware incurs a substantial CPU overhead (30-60%). In the future, I will use MARS to investigate other performance aspects of sophisticated runtime systems.