This paper introduces the batch-parallel Compressed Packed Memory Array (CPMA), a compressed, dynamic, ordered set data structure based on the Packed Memory Array (PMA). Traditionally, batch-parallel sets are built on pointer-based data structures such as trees because pointer-based structures enable fast parallel unions via pointer manipulation. When compared with cache-optimized trees, PMAs were slower to update but faster to scan. The batch-parallel CPMA overcomes this tradeoff between updates and scans by optimizing for cache-friendliness. On average, the CPMA achieves 3x faster batch-insert throughput and 4x faster range-query throughput compared with compressed PaC-trees, a state-of-the-art batch-parallel set library based on cache-optimized trees. We further evaluate the CPMA compared with compressed PaC-trees and Aspen, a state-of-the-art system, on a real-world application of dynamic-graph processing. The CPMA is on average 1.2x faster on a suite of graph algorithms and 2x faster on batch inserts when compared with compressed PaC-trees. Furthermore, the CPMA is on average 1.3x faster on graph algorithms and 2x faster on batch inserts compared with Aspen.

翻译：本文介绍了批并行压缩打包内存阵列（CPMA），一种基于打包内存阵列（PMA）的压缩、动态、有序集合数据结构。传统上，批并行集合构建于基于指针的数据结构（如树）之上，因为基于指针的结构可通过指针操作实现快速的并行合并。与缓存优化树相比，PMA的更新速度较慢但扫描速度更快。批并行CPMA通过优化缓存友好性克服了这种更新与扫描之间的权衡。平均而言，与基于缓存优化树的最先进批并行集合库压缩PaC树相比，CPMA的批量插入吞吐量提升3倍，范围查询吞吐量提升4倍。我们进一步在动态图处理的真实应用中将CPMA与压缩PaC树以及最先进系统Aspen进行对比。与压缩PaC树相比，CPMA在一组图算法上平均快1.2倍，在批量插入上快2倍。此外，与Aspen相比，CPMA在图算法上平均快1.3倍，在批量插入上快2倍。