We present a region-based memory management scheme with support for generational garbage collection. The scheme features a compile-time region inference algorithm, which associates values with logical regions, and builds on a region type system that deploys region types at runtime to avoid the overhead of write barriers and to support partly tag-free garbage collection. The scheme is implemented in the MLKit Standard ML compiler, which generates native x64 machine code. Besides demonstrating a number of important formal properties of the scheme, we measure the scheme’s characteristics, for a number of benchmarks, and compare the performance of the generated executables with the performance of executables generated with the MLton state-of-the-art Standard ML compiler and configurations of the MLKit with and without region inference and generational garbage collection enabled. Although region inference often serves the purpose of generations, combining region inference with generational garbage collection is shown often to be superior to combining region inference with non-generational collection despite the overhead introduced by a larger amount of memory waste, due to region fragmentation.

中文翻译：

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集成区域内存管理和无标记的分代垃圾收集

我们提出了一种基于区域的内存管理方案，支持分代垃圾收集。该方案采用编译时区域推断算法，将值与逻辑区域相关联，并建立在区域类型系统之上，该系统在运行时部署区域类型以避免写入障碍的开销并支持部分无标签垃圾收集。该方案在 MLKit 标准 ML 编译器中实现，该编译器生成本机 x64 机器代码。除了展示该方案的一些重要的形式属性外，我们还针对多个基准测试了该方案的特征，并将生成的可执行文件的性能与使用 MLton 最先进的标准 ML 编译器生成的可执行文件的性能以及启用和不启用区域推断和分代垃圾收集的 MLKit 配置进行比较。尽管区域推断通常用于生成代的目的，但将区域推断与分代垃圾收集相结合通常优于将区域推断与非分代收集相结合，尽管由于区域碎片而导致大量内存浪费引入了开销。