article · ACM SIGAPP Applied Computing Review · 2017
Large scale document inversion using a multi-threaded computing system
Sungbo Jung, Dar-Jen Chang, Juw Won Park
Cited by 0Open access
Abstract
Current microprocessor architecture is moving towards multi-core/multi-threaded systems. This trend has led to a surge of interest in using multi-threaded computing devices, such as the Graphics Processing Unit (GPU), for general purpose computing. We can utilize the GPU in computation as a massive parallel co-processor because the GPU consists of multiple cores. The GPU is also an affordable, attractive, and user-programmable commodity. Nowadays a lot of information has been flooded into the digital domain around the world. Huge volume of data, such as digital libraries, social networking services, e-commerce product data, and reviews, etc., is produced or collected every moment with dramatic growth in size. Although the inverted index is a useful data structure that can be used for full text searches or document retrieval, a large number of documents will require a tremendous amount of time to create the index. The performance of document inversion can be improved by multi-thread or multi-core GPU. Our approach is to implement a linear-time, hash-based, single program multiple data (SPMD), document inversion algorithm on the NVIDIA GPU/CUDA programming platform utilizing the huge computational power of the GPU, to develop high performance solutions for document indexing. Our proposed parallel document inversion system shows 2--3 times faster performance than a sequential system on two different test datasets from PubMed abstract and e-commerce product reviews.
Cite this paper
Jung, S., Chang, D.-J., & Park, J. W. (2017). Large scale document inversion using a multi-threaded computing system. ACM SIGAPP Applied Computing Review, 17(2), 27–35. https://doi.org/10.1145/3131080.3131083
Jung, Sungbo, et al. “Large scale document inversion using a multi-threaded computing system.” ACM SIGAPP Applied Computing Review, vol. 17, no. 2, 2017, pp. 27–35, https://doi.org/10.1145/3131080.3131083.
Jung, Sungbo, Dar-Jen Chang, and Juw Won Park. 2017. “Large scale document inversion using a multi-threaded computing system.” ACM SIGAPP Applied Computing Review 17 (2): 27–35. https://doi.org/10.1145/3131080.3131083.
Jung, S., Chang, D.-J. and Park, J.W. (2017) ‘Large scale document inversion using a multi-threaded computing system’, ACM SIGAPP Applied Computing Review, 17(2), pp. 27–35. Available at: https://doi.org/10.1145/3131080.3131083.
@article{jung2017large,
title = {{Large scale document inversion using a multi-threaded computing system}},
author = {Jung, Sungbo and Chang, Dar-Jen and Park, Juw Won},
journal = {ACM SIGAPP Applied Computing Review},
volume = {17},
number = {2},
pages = {27--35},
year = {2017},
doi = {10.1145/3131080.3131083}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.