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Fast lists intersection with Bloom filter using graphics processing units

Published: 21 March 2011 Publication History

Abstract

Intersection of sorted inverted lists is an important operation in the web search engines. Various algorithms to improve the performance of this operation have been introduced in the literature [1, 3, 5]. Previous research works mainly focused on single-core or multi-core CPU platform and did not consider the query traffic problem arises in the actual systems. Modern graphics processing units (GPUs) give a new way to solve the problem. Wu et al. [6] presented a CPU-GPU cooperative model which can dynamically switch between the asynchronous mode and the synchronous mode. Under light query traffic, asynchronous mode is triggered, each newly arriving query is serviced by an independent thread. Under heavy query traffic, synchronous mode is triggered, all active threads are blocked and a single thread takes control of query processing. Queries are grouped into batches at CPU end, and each batch is processed by GPU threads in parallel. We summarize that putting the operations on GPU has two advantages: The massive on-chip parallelism of GPU may greatly reduce the processing time of lists intersection; A great part of work on CPU is offloaded to GPU. Overall the GPU will significantly increase throughput and reduce average response time in the synchronous mode. In this paper we consider techniques for improving the performance of the GPU batched algorithm proposed in [6] assuming sufficient queries at the CPU end.

References

[1]
J. Barbay, A. López-Ortiz, T. Lu, and A. Salinger. Faster Set Intersection Algorithms for Text Searching. ACM Journal of Experimental Algorithmics, 2006.
[2]
B. Bloom. Space/time trade-offs in hash coding with allowable errors. Communications of the ACM, 13(7): 422--426, 1970.
[3]
E. Demaine, A. López-Ortiz, and J. Ian Munro. Experiments on adaptive set intersections for text retrieval systems. Algorithm Engineering and Experimentation, pages 91--104.
[4]
W. Pugh. Skip lists: a probabilistic alternative to balanced trees. Comm. ACM, 33(6): 668--676, 1990.
[5]
D. Tsirogiannis, S. Guha, and N. Koudas. Improving the performance of list intersection. Proceedings of the VLDB Endowment, 2(1): 838--849, 2009.
[6]
D. Wu, F. Zhang, N. Ao, G. Wang, X. Liu, and J. Liu. Efficient lists intersection by cpu-gpu cooperative computing. In Parallel Distributed Processing, Workshops and Phd Forum (IPDPSW), 2010 IEEE International Symposium on, pages 1--8, 19--23 2010.

Cited By

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  • (2022)Hunting the pertinency of hash and bloom filter combinations on GPU for fast pattern matchingInternational Journal of Information Technology10.1007/s41870-022-00964-314:5(2667-2679)Online publication date: 17-May-2022
  • (2020)SpArch: Efficient Architecture for Sparse Matrix Multiplication2020 IEEE International Symposium on High Performance Computer Architecture (HPCA)10.1109/HPCA47549.2020.00030(261-274)Online publication date: Feb-2020
  • (2019)Performance evaluation of single vs. batch of queries on GPUsConcurrency and Computation: Practice and Experience10.1002/cpe.547432:20Online publication date: 8-Aug-2019
  • Show More Cited By

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cover image ACM Conferences
SAC '11: Proceedings of the 2011 ACM Symposium on Applied Computing
March 2011
1868 pages
ISBN:9781450301138
DOI:10.1145/1982185

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 March 2011

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Author Tags

  1. Bloom filter
  2. GPU
  3. lists intersection

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SAC'11
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SAC'11: The 2011 ACM Symposium on Applied Computing
March 21 - 24, 2011
TaiChung, Taiwan

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

View all
  • (2022)Hunting the pertinency of hash and bloom filter combinations on GPU for fast pattern matchingInternational Journal of Information Technology10.1007/s41870-022-00964-314:5(2667-2679)Online publication date: 17-May-2022
  • (2020)SpArch: Efficient Architecture for Sparse Matrix Multiplication2020 IEEE International Symposium on High Performance Computer Architecture (HPCA)10.1109/HPCA47549.2020.00030(261-274)Online publication date: Feb-2020
  • (2019)Performance evaluation of single vs. batch of queries on GPUsConcurrency and Computation: Practice and Experience10.1002/cpe.547432:20Online publication date: 8-Aug-2019
  • (2018)A Parallel Implementation of WAND on GPUs2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP)10.1109/PDP2018.2018.00011(10-17)Online publication date: Mar-2018
  • (2017)Efficient GPU-Based Query Processing with Pruned List Caching in Search Engines2017 IEEE 23rd International Conference on Parallel and Distributed Systems (ICPADS)10.1109/ICPADS.2017.00038(215-224)Online publication date: Dec-2017
  • (2017)GPU-Accelerated Block-Max Query ProcessingAlgorithms and Architectures for Parallel Processing10.1007/978-3-319-65482-9_15(225-238)Online publication date: 11-Aug-2017
  • (2015)Evaluation of statistical properties of a modified Bloom filter for heterogeneous GPGPU-systems2015 IEEE NW Russia Young Researchers in Electrical and Electronic Engineering Conference (EIConRusNW)10.1109/EIConRusNW.2015.7102234(71-74)Online publication date: Feb-2015

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