Towards Hardware Accelerated Garbage Collection with Near-Memory Processing

Abstract

Garbage collection is widely available in popular programming languages, yet it may incur high performance overheads in applications. Prior works have proposed specialized hardware acceleration implementations to offload garbage collection overheads off the main processor, but these solutions have yet to be implemented in practice. In this paper, we propose using off-the-shelf hardware to accelerate off-the-shelf garbage collection algorithms. Furthermore, our work is latency oriented as opposed to other works that focus on bandwidth. We demonstrate that we can get a 2 x performance improvement in some workloads and a 2.3 x reduction in LLC traffic by integrating generic Near-Memory Processing (NMP) into the built-in Java garbage collector. We will discuss architectural implications of these results and consider directions for future work.