Performance Analysis and Optimization of Full Garbage Collection in Memory-hungry Environments

摘要

Garbage collection (GC), especially full GC, would nontrivially impact overall application performance, especially for those memory-hungry ones handling large data sets. This paper presents an in-depth performance analysis on the full GC performance of Parallel Scavenge (PS), a state-of-the-art and the default garbage collector in the HotSpot JVM, using traditional and big-data applications running atop JVM on CPU (e.g., Intel Xeon) and many-integrated cores (e.g., Intel Xeon Phi). The analysis uncovers that unnecessary memory accesses and calculations during reference updating in the compaction phase are the main causes of lengthy full GC. To this end, this paper describes an incremental query model for reference calculation, which is further embodied with three schemes (namely optimistic, sort-based and region-based) for different query patterns. Performance evaluation shows that the incremental query model leads to averagely 1.9X (up to 2.9X) in full GC and 19.3% (up to 57.2%) improvement in application throughput, as well as 31.2% reduction in pause time over the vanilla PS collector on CPU, and the numbers are 2.1X (up to 3.4X), 11.1% (up to 41.2%) and 34.9% for Xeon Phi accordingly.