Comparison sorting on hybrid multicore architectures for fixed and variable length keys. (August 2014)

• Record Type: Journal Article
• Authors: Banerjee, Dip Sankar; Sakurikar, Parikshit; Kothapalli, Kishore
• Is Part Of: International journal of high performance computing applications. Volume 28:Number 3(2014:Autumn)
• Page Start: 267
• Page End: 284
• Publication Date: 2014-08
• Subjects: Comparison sort -- hybrid -- fixed and variable length keys -- split based sort -- CPU + GPU; High performance computing -- Periodicals; Supercomputers -- Periodicals; 004.1105
• DOI: 10.1177/1094342014526906
• Languages: English
• ISSNs: 1094-3420

Abstract

Sorting has been a topic of immense research value since the inception of computer science. Hybrid computing on multicore architectures involves computing simultaneously on a tightly coupled heterogeneous collection of devices. In this work, we consider a multicore CPU along with a manycore GPU as our experimental hybrid platform. In this work, we present a hybrid comparison based sorting algorithm which utilizes a many-core GPU and a multi-core CPU to perform sorting. The algorithm is broadly based on splitting the input list according to a large number of splitters followed by creating independent sublists. Sorting the independent sublists results in sorting the entire original list. On a CPU + GPU platform consisting of an Intel i7-980X and an NVidia GTX 580, our algorithm achieves a 20% gain over the current best known comparison sort result that was published by (Davidson et al., 2012). On the above experimental platform, our results are better by 40% on average over a similar GPU-alone algorithm proposed by (Leischner et al., 2010). We also extend our sorting algorithm for fixed length keys to variable length keys. We use a look-ahead based approach to sort strings and obtain around a 24% benefit compared to the current best known solution. Our results also show that our algorithm and its implementation scale with the size of the input. We also show that such performance gains can be obtained on other hybrid CPU + GPU platforms.