Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/35627
Title: Accelerating aggregation using intra-cycle parallelism
Authors: Feng, Z
Lo, E 
Issue Date: 2015
Publisher: IEEE Computer Society
Source: Proceedings - International Conference on Data Engineering, 2015, v. 2015-May, 7113292, p. 291-302 How to cite?
Abstract: Modern CPUs have word width of 64 bits but real data values are usually represented using bits fewer than a CPU word. This underutilization of CPU at register level has motivated the recent development of bit-parallel algorithms that carry out data processing operations (e.g., filter scan) on CPU words packed with data values (e.g., 8 data values are packed into one 64-bit word). Bit-parallel algorithms fully unleash the intra-cycle parallelism of modern CPUs and they are especially attractive to main-memory column stores whose goal is to process data at the speed of the 'bare metal'. Main-memory column stores generally focus on analytical queries, where aggregation is a common operation. Current bit-parallel algorithms, however, have not covered aggregation yet. In this paper, we present a suite of bit-parallel algorithms to accelerate all standard aggregation operations: SUM, MIN, MAX, AVG, MEDIAN, COUNT. The algorithms are designed to fully leverage the intra-cycle parallelism in CPU cores when aggregating words of packed values. Experimental evaluation shows that our bit-parallel aggregation algorithms exhibit significant performance benefits compared with non-bit-parallel methods.
Description: 2015 31st IEEE International Conference on Data Engineering, ICDE 2015, 13-17 April 2015
URI: http://hdl.handle.net/10397/35627
ISBN: 9781479979639
DOI: 10.1109/ICDE.2015.7113292
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