{"title":"Integrating region memory management and tag-free generational garbage collection","authors":"M. Elsman, Niels Hallenberg","doi":"10.1017/S0956796821000010","DOIUrl":null,"url":null,"abstract":"Abstract 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.","PeriodicalId":15874,"journal":{"name":"Journal of Functional Programming","volume":" ","pages":""},"PeriodicalIF":1.1000,"publicationDate":"2021-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/S0956796821000010","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Functional Programming","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1017/S0956796821000010","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
引用次数: 9
Abstract
Abstract 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.
摘要我们提出了一种基于区域的内存管理方案,支持代垃圾收集。该方案采用编译时区域推理算法,将值与逻辑区域相关联,并建立在区域类型系统的基础上,该系统在运行时部署区域类型,以避免写障碍的开销,并支持部分无标记的垃圾收集。该方案在MLKit Standard ML编译器中实现,该编译器生成本机x64计算机代码。除了证明该方案的一些重要形式性质外,我们还测量了该方案的特性,用于许多基准,并将生成的可执行文件的性能与使用MLton最先进的标准ML编译器生成的可运行文件的性能以及启用和不启用区域推断和世代垃圾收集的MLKit的配置进行比较。尽管区域推断通常有助于生成,但将区域推断与生成垃圾收集相结合通常比将区域推断和非生成收集相结合要好,尽管由于区域碎片化,大量内存浪费会带来开销。
期刊介绍:
Journal of Functional Programming is the only journal devoted solely to the design, implementation, and application of functional programming languages, spanning the range from mathematical theory to industrial practice. Topics covered include functional languages and extensions, implementation techniques, reasoning and proof, program transformation and synthesis, type systems, type theory, language-based security, memory management, parallelism and applications. The journal is of interest to computer scientists, software engineers, programming language researchers and mathematicians interested in the logical foundations of programming.