Predicting Scalability of Parallel Garbage Collectors on Shared Memory Multiprocessors

This paper describes a performanceprediction model of parallel mark-sweep garbage collectors (GC) on shared memory multiprocessors. The prediction model takes the heap snapshot and memory access cost parameters (latency and occupancy) as inputs, and outputs performanceoftheparallel marking on any given number of processors. It takes several factors that affects performance into account: cache misses costs, memory access contention, and increase of misses by parallelization. We evaluate this model by comparing the predicted GC performance and measured performanceontwoarchitecturally different shared memory machines: Ultra Enterprise 10000 (crossbar connected SMP) and Origin 2000 (hypercube connected DSM). Our model accurately predicts qualitatively different speedups on the two machines that occurred in one application, which turn out to beduetocontentions on a memory node. In addition to performance analysis, applications of the proposedmodel include adaptive GC algorithm to achieve optimal performancebasedon the prediction. This paper shows the effect of automatic regulation of GC parallelism