Optimizing Search Layouts in Packed Memory Arrays.

Previous chapter Next chapter Full AccessProceedings 2023 Proceedings of the Symposium on Algorithm Engineering and Experiments (ALENEX)Optimizing Search Layouts in Packed Memory ArraysBrian Wheatman, Randal Burns, Aydın Buluç, and Helen XuBrian Wheatman, Randal Burns, Aydın Buluç, and Helen Xupp.148 - 161Chapter DOI:https://doi.org/10.1137/1.9781611977561.ch13PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract This paper introduces Search-optimized Packed Memory Arrays (SPMAs), a collection of data structures based on Packed Memory Arrays (PMAs) that address suboptimal search via cache-optimized search layouts. Traditionally, PMAs and B-trees have tradeoffs between searches/inserts and scans: B-trees were faster for searches and inserts, while PMAs were faster for scans. Our empirical evaluation shows that SPMAs overcome this tradeoff for unsorted input distributions: on average, SPMAs are faster than B+-trees (a variant of B-trees optimized for scans) on all major operations. We generated datasets and search/insert workloads from the Yahoo! Cloud Serving Benchmark (YCSB) and found that SPMAs are about 2× faster than B+-trees regardless of the ratio of searches to inserts. On uniform random inputs, SPMAs are on average between 1.3× −2.3× faster than B+-trees on all operations. Finally, we vary the amount of sortedness in the inputs to stress the worst-case insert distribution in the PMA. We find that the worst-case B+-tree insertion throughput is about 1.5× faster than the worst-case PMA insertion throughput. However, the worst-case input for the PMA is sorted and highly unlikely to appear naturally in practice. The SPMAs maintain higher insertion throughput than the B+-tree when the input is up to 25% sorted. Previous chapter Next chapter RelatedDetails Published:2023eISBN:978-1-61197-756-1 https://doi.org/10.1137/1.9781611977561Book Series Name:ProceedingsBook Code:PRAL23Book Pages:iii-213