Applying Predication to Reduce the Cost of Virtual Function Calls

Most modern programming languages require efficient automatic memory management (garbage collection, GC) as part of the runtime system. Since GC is very memory intensive it can potentially suffer significantly from poor memory access times. Unfortunately, memory performance improves at a slower pace than processor speed, making memory accesses relatively more expensive in the future. Active Memory architectures aim to overcome this problem by placing additional computational power in memory, thus allowing the application to execute small but memoryintensive functions closer to the data and in parallel. The goal is to improve latency and bandwidth for programs that can otherwise suffer from slow memory accesses. To date, Active Memory has been studied only with databases, image processing, arithmetic computations, and other very regular applications. In this paper, we propose to analyze its impact on garbage collection. We are convinced that garbage collection too will profit from this architecture since GC is simple, repetitive, easy to partition into offloadable functions, and its performance depends crucially on fast memory access. We describe a possible incarnation of an Active Memory architecture suitable for GC support and argue why GC should benefit from such an architecture.