对多还是对一?一体化!具有类型异构哈希尝试的高效纯功能多映射

M. Steindorfer, J. Vinju
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引用次数: 1

摘要

不可变多映射是一种多对多映射数据结构,具有预期的快速插入和查找操作。此数据结构用于处理图形或多对多关系的应用程序,如在编译器、编程语言的运行时或面向对象系统的静态分析中应用的应用程序。假定集合数据结构仔细地平衡了操作的执行时间和内存消耗特征,并且需要从几个元素优雅地扩展到至少多个gb。当处理较大的内存数据集时,数据结构编码本身的开销就会成为内存使用的瓶颈,影响整体性能。在本文中,我们提出了AXIOM,这是一种新颖的哈希-trie数据结构,它通过区分单例集的内联值和多映射的嵌套集来实现高效和类型安全的多映射编码。AXIOM通过在实现级别上支持处理细粒度类型异构的内容,对以前的散列数据结构进行了严格的泛化(而API和语言对类型异构的支持不在本文的讨论范围之内)。我们详细介绍了AXIOM的设计和优化,并进一步将其与Java、Scala和Clojure中最先进的不可变映射和多映射进行比较。我们使用微基准测试隔离关键差异,并在静态分析的案例研究中验证得出的结论。AXIOM减少了1.87倍的键值存储开销;通过跨集合边界的专门化和内联,性能提高了5.1倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
To-many or to-one? all-in-one! efficient purely functional multi-maps with type-heterogeneous hash-tries
An immutable multi-map is a many-to-many map data structure with expected fast insert and lookup operations. This data structure is used for applications processing graphs or many-to-many relations as applied in compilers, runtimes of programming languages, or in static analysis of object-oriented systems. Collection data structures are assumed to carefully balance execution time of operations with memory consumption characteristics and need to scale gracefully from a few elements to multiple gigabytes at least. When processing larger in-memory data sets the overhead of the data structure encoding itself becomes a memory usage bottleneck, dominating the overall performance. In this paper we propose AXIOM, a novel hash-trie data structure that allows for a highly efficient and type-safe multi-map encoding by distinguishing inlined values of singleton sets from nested sets of multi-mappings. AXIOM strictly generalizes over previous hash-trie data structures by supporting the processing of fine-grained type-heterogeneous content on the implementation level (while API and language support for type-heterogeneity are not scope of this paper). We detail the design and optimizations of AXIOM and further compare it against state-of-the-art immutable maps and multi-maps in Java, Scala and Clojure. We isolate key differences using microbenchmarks and validate the resulting conclusions on a case study in static analysis. AXIOM reduces the key-value storage overhead by 1.87x; with specializing and inlining across collection boundaries it improves by 5.1x.
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