MASC: A bitmap index encoding algorithm for fast data retrieval

Wen, Yang; Wang, Han; Chen, Zhen; Cao, Junwei; Peng, Guodong; Huang, Wen; Hu, Ziwei; Zhou, Jing; Guo, Jinghong

doi:10.1109/icc.2016.7510827

Cited by 4 publications

(1 citation statement)

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“…For example Fusco et al [18] demonstrated that greater throughout of bitmap creation could be achieved using GPU implementations over CPU implementations of WAH, and a related compression scheme, position list word-aligned hybrid (PLWAH) [15]. Similarly, Chen et al [10] showed that a GPU implementation of their maximized stride with carrier (MASC) [43] bitmap index compression scheme was 19.5× faster at constructing a compressed bitmap index their CPU implementation. These works focused only on the initial creation of the bitmap index and did not address query processing, which is the focus of our work.…”

Section: Related Workmentioning

confidence: 99%

Exploring Means to Enhance the Efficiency of GPU Bitmap Index Query Processing

et al. 2020

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Once exotic, computational accelerators are now commonly available in many computing systems. Graphics processing units (GPUs) are perhaps the most frequently encountered computational accelerators. Recent work has shown that GPUs are beneficial when analyzing massive data sets. Specifically related to this study, it has been demonstrated that GPUs can significantly reduce the query processing time of database bitmap index queries. Bitmap indices are typically used for large, read-only data sets and are often compressed using some form of hybrid run-length compression. In this paper, we present three GPU algorithm enhancement strategies for executing queries of bitmap indices compressed using word aligned hybrid compression: (1) data structure reuse (2) metadata creation with various type alignment and (3) a preallocated memory pool. The data structure reuse greatly reduces the number of costly memory system calls. The use of metadata exploits the immutable nature of bitmaps to pre-calculate and store necessary intermediate processing results. This metadata reduces the number of required query-time processing steps. Preallocating a memory pool can reduce or entirely remove the overhead of memory operations during query processing. Our empirical study showed that performing a combination of these strategies can achieve 32.4$$\times$$ × to 98.7$$\times$$ × speedup over the current state-of-the-art implementation. Our study also showed that by using our enhancements, a common gaming GPU can achieve a $$15.0\times$$ 15.0 × speedup over a more expensive high-end CPU.

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Section: Related Workmentioning

confidence: 99%