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Large Scale Document Inversion using a Multi-threaded Computing System.

Sungbo Jung1, Dar-Jen Chang2, Juw Won Park3

  • 1University of Louisville, Computer Engineering and Computer Science, Louisville, KY 40292, 1-502-852-0467.

ACM SIGAPP Applied Computing Review : a Publication of the Special Interest Group on Applied Computing
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Summary
This summary is machine-generated.

This study accelerates document indexing by using Graphics Processing Units (GPUs) for parallel processing. The GPU-based approach significantly speeds up the creation of inverted indexes for large datasets, improving information retrieval performance.

Keywords:
GPUGraphics Processing Unitdocument inversionhigh-performance computing

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Area of Science:

  • Computer Science
  • Information Retrieval
  • High-Performance Computing

Background:

  • Modern microprocessors increasingly utilize multi-core/multi-threaded architectures.
  • Graphics Processing Units (GPUs) offer massive parallel processing capabilities for general-purpose computing.
  • The exponential growth of digital data necessitates efficient indexing and retrieval methods.

Purpose of the Study:

  • To develop a high-performance document inversion system for efficient data indexing.
  • To leverage the parallel processing power of GPUs for accelerating the creation of inverted indexes.
  • To improve the performance of document retrieval systems through optimized indexing.

Main Methods:

  • Implementation of a linear-time, hash-based, single program multiple data (SPMD) document inversion algorithm.
  • Utilizing the NVIDIA GPU/CUDA programming platform for massive parallel computation.
  • Developing a parallel document inversion system to process large datasets.

Main Results:

  • The proposed parallel document inversion system demonstrated 2-3 times faster performance compared to sequential systems.
  • The system was tested on two distinct datasets: PubMed abstracts and e-commerce product reviews.
  • Significant performance gains were achieved by harnessing the computational power of GPUs.

Conclusions:

  • GPU-accelerated document inversion offers a substantial performance improvement for indexing large volumes of data.
  • The developed system provides an efficient solution for creating inverted indexes, crucial for information retrieval.
  • This approach is well-suited for handling the ever-increasing scale of digital information.