G-TADOC: Enabling Efficient GPU-Based Text Analytics without Decompression

@article{Zhang2021GTADOCEE,
  title={G-TADOC: Enabling Efficient GPU-Based Text Analytics without Decompression},
  author={Feng Zhang and Zaifeng Pan and Yanliang Zhou and Jidong Zhai and Xipeng Shen and O. Mutlu and Xiaoyong Du},
  journal={2021 IEEE 37th International Conference on Data Engineering (ICDE)},
  year={2021},
  pages={1679-1690}
}
  • Feng Zhang, Zaifeng Pan, +4 authors Xiaoyong Du
  • Published 2021
  • Computer Science
  • 2021 IEEE 37th International Conference on Data Engineering (ICDE)
Text analytics directly on compression (TADOC) has proven to be a promising technology for big data analytics. GPUs are extremely popular accelerators for data analytics systems. Unfortunately, no work so far shows how to utilize GPUs to accelerate TADOC. We describe G-TADOC, the first framework that provides GPU-based text analytics directly on compression, effectively enabling efficient text analytics on GPUs without decompressing the input data.G-TADOC solves three major challenges. First… Expand

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