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An Efficient Vectorized Hash Table for Batch Computations

Authors Hesam Shahrokhi , Amir Shaikhha

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Author Details

Hesam Shahrokhi
  • University of Edinburgh, UK
Amir Shaikhha
  • University of Edinburgh, UK

Acknowledgements

The authors would like to thank Huawei for their support of the distributed data management and processing laboratory at the University of Edinburgh.

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Hesam Shahrokhi and Amir Shaikhha. An Efficient Vectorized Hash Table for Batch Computations. In 37th European Conference on Object-Oriented Programming (ECOOP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 263, pp. 27:1-27:27, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023) https://doi.org/10.4230/LIPIcs.ECOOP.2023.27

Abstract

In recent years, the increasing demand for high-performance analytics on big data has led the research on batch hash tables. It is shown that this type of hash table can benefit from the cache locality and multi-threading more than ordinary hash tables. Moreover, the batch design for hash tables is amenable to using advanced features of modern processors such as prefetching and SIMD vectorization. While state-of-the-art research and open-source projects on batch hash tables made efforts to propose improved designs by better usage of mentioned hardware features, their approaches still do not fully exploit the existing opportunities for performance improvements. Furthermore, there is a gap for a high-level batch API of such hash tables for wider adoption of these high-performance data structures. In this paper, we present Vec-HT, a parallel, SIMD-vectorized, and prefetching-enabled hash table for fast batch processing. To allow developers to fully take advantage of its performance, we recommend a high-level batch API design. Our experimental results show the superiority and competitiveness of this approach in comparison with the alternative implementations and state-of-the-art for the data-intensive workloads of relational join processing, set operations, and sparse vector processing.

Subject Classification

ACM Subject Classification
  • Theory of computation → Data structures design and analysis
  • Computer systems organization → Single instruction, multiple data
Keywords
  • Hash tables
  • Vectorization
  • Parallelization
  • Prefetching

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