Towards Hardware Accelerated Garbage Collection with Near-Memory Processing
Towards Hardware Accelerated Garbage Collection with Near-Memory Processing
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DOI：
10.1109/hpec55821.2022.9926323
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发表时间：
2022-09
期刊：
影响因子：
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通讯作者：
Samuel Thomas;Jiwon Choe;Ofir Gordon;E. Petrank;T. Moreshet;M. Herlihy;R. I. Bahar
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文献类型：
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作者：
Samuel Thomas;Jiwon Choe;Ofir Gordon;E. Petrank;T. Moreshet;M. Herlihy;R. I. Bahar
垃圾收集在流行的编程语言中广泛使用，但它可能会在应用程序中产生高性能开销。以前的工作已经提出了专门的硬件加速实现，以减轻主处理器的垃圾收集开销，但这些解决方案尚未在实践中实现。在本文中，我们建议使用现成的硬件来加速现成的垃圾收集算法。此外，我们的工作是面向延迟的，而不是其他专注于带宽的工作。我们演示了通过将泛型近内存处理（NMP）集成到内置Java垃圾收集器中，我们可以在某些工作负载中获得2倍的性能改进，并将LLC流量减少2.3倍。我们将讨论这些结果的架构含义，并考虑未来工作的方向。
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.