A Comparative Study of Stampede Garbage Collection Algorithms

Abstract

Stampede is a parallel programming system to support interactive multimedia applications. The system maintains temporal causality in such streaming real-time applications via channels that contain timestamped items. A Stampede application is a coarse-grain dataflow pipeline of timestamped items. Not all timestamps are relevant for the application output due to the differential processing rates of the pipeline stages. Therefore, garbage collection (GC) is crucial for Stampede runtime performance. Three GC algorithms are currently available in Stampede. In this paper, we ask the question how far off these algorithms are from an ideal garbage collector, one in which the memory usage is exactly equal to that which is required for buffering only the relevant timestamped items in the channels? This oracle, while unimplementable, serves as an empirical lower-bound for memory usage. We then propose optimizations that will help us get closer to this lower-bound. Using an elaborate measurement and post-mortem analysis infrastructure in Stampede, we evaluate the performance potential for these optimizations. A color-based people tracking application is used for the performance evaluation. Our results show that these optimizations reduce the memory usage by over 60% for this application over the best GC algorithm available in Stampede.