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. he 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：本文介绍了批量并行压缩打包内存数组（CPMA），这是一种基于打包内存数组（PMA）的压缩动态有序集合数据结构。传统上，批量并行集合基于树等指针数据结构构建，因为指针结构可通过指针操作实现快速并行并集。与缓存优化树相比，PMA更新速度较慢但扫描速度更快。批量并行CPMA通过优化缓存友好性克服了更新与扫描之间的权衡。与基于缓存优化树的先进批量并行集合库压缩PaC树相比，CPMA的平均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树及先进系统Aspen进行对比评估。在图算法套件中，CPMA相比压缩PaC树平均加速1.2倍，批量插入加速2倍；相比Aspen，图算法平均加速1.3倍，批量插入加速2倍。