利用热度改进GC中的程序局部性

A. Yang, Erik Österlund, Tobias Wrigstad
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引用次数: 8

摘要

分级存储系统的内存越来越小,越来越快,更靠近CPU,长期以来一直是隐藏或减轻内存和处理器之间性能差距的核心。为了利用这种硬件,必须编写程序以显示良好的对象局部性。在像C/ c++这样的语言中,程序员可以仔细规划对象应该如何布局(尽管耗时且容易出错);对于托管语言,特别是具有移动垃圾收集器的语言,手动创建的最佳布局可能会在对象重定位过程中被破坏。对于呈现抽象内存视图的托管语言,解决方案在于使垃圾收集器意识到对象局部性,并努力实现和保持良好的局部性,即使面对在不同阶段表现出不同行为的多阶段程序。本文提出了一种动态重组对象的GC设计方法,该方法根据变量访问对象的顺序对对象进行动态重组,并在内存中努力分离频繁使用和不频繁使用的对象。这提高了局部性和硬件预取的效率。识别经常使用的对象是在运行时完成的,开销很小。HCSGC还提供了可调性,用于将重定位工作转移到突变体,或多或少地进行主动对象重定位。这些想法在OpenJDK上的ZGC收集器的上下文中进行了评估,并产生了5% (tradebeans), 9% (h2)和令人印象深刻的25-45% (JGraphT)的性能改进,所有这些都有95%的置信度。对于SPECjbb,由于基线的波动,结果是不确定的。
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Improving program locality in the GC using hotness
The hierarchical memory system with increasingly small and increasingly fast memory closer to the CPU has for long been at the heart of hiding, or mitigating the performance gap between memories and processors. To utilise this hardware, programs must be written to exhibit good object locality. In languages like C/C++, programmers can carefully plan how objects should be laid out (albeit time consuming and error-prone); for managed languages, especially ones with moving garbage collectors, a manually created optimal layout may be destroyed in the process of object relocation. For managed languages that present an abstract view of memory, the solution lies in making the garbage collector aware of object locality, and strive to achieve and maintain good locality, even in the face of multi-phased programs that exhibit different behaviour across different phases. This paper presents a GC design that dynamically reorganises objects in the order mutators access them, and additionally strives to separate frequently and infrequently used objects in memory. This improves locality and the efficiency of hardware prefetching. Identifying frequently used objects is done at run-time, with small overhead. HCSGC also offers tunability, for shifting relocation work towards mutators, or for more or less aggressive object relocation. The ideas are evaluated in the context of the ZGC collector on OpenJDK and yields performance improvements of 5% (tradebeans), 9% (h2) and an impressive 25–45% (JGraphT), all with 95% confidence. For SPECjbb, results are inconclusive due to a fluctuating baseline.
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