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Engineering faster sorters for small sets of items

Bingmann, Timo; Marianczuk, Jasper; Sanders, Peter

Abstract:
Sorting a set of items is a task that can be useful by itself or as a building block for more complex operations. That is why a lot of effort has been put into finding sorting algorithms that sort large sets as fast as possible. But the more sophisticated and complex the algorithms become, the less efficient they are for small sets of items due to large constant factors. We aim to determine if there is a faster way than insertion sort to sort small sets of items to provide a more efficient base case sorter. We looked at sorting networks, at how they can improve the speed of sorting few elements, and how to implement them in an efficient manner by using conditional moves. Since sorting networks need to be implemented explicitly for each set size, providing networks for larger sizes becomes less efficient due to increased code sizes. To also enable the sorting of slightly larger base cases, we adapted sample sort to Register Sample Sort, to break down those larger sets into sizes that can in turn be sorted by sorting networks. From our experiments we found that when sorting only small sets, the sorting networks outperform insertion sort by a factor of at least 1.76 for any array size between six and sixteen, and by a factor of 2.72 on average across all machines and array sizes. ... mehr



Zugehörige Institution(en) am KIT Institut für Theoretische Informatik (ITI)
Publikationstyp Forschungsbericht/Preprint
Publikationsjahr 2020
Sprache Englisch
Identifikator ISSN: 0038-0644, 1097-024X
KITopen-ID: 1000127717
Vorab online veröffentlicht am 02.11.2020
Nachgewiesen in arXiv
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