License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.ESA.2017.9
URN: urn:nbn:de:0030-drops-78542
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Axtmann, Michael ; Witt, Sascha ; Ferizovic, Daniel ; Sanders, Peter

In-Place Parallel Super Scalar Samplesort (IPSSSSo)

LIPIcs-ESA-2017-9.pdf (0.9 MB)


We present a sorting algorithm that works in-place, executes in parallel, is cache-efficient, avoids branch-mispredictions, and performs work O(n log n) for arbitrary inputs with high probability. The main algorithmic contributions are new ways to make distribution-based algorithms in-place: On the practical side, by using coarse-grained block-based permutations, and on the theoretical side, we show how to eliminate the recursion stack. Extensive experiments shw that our algorithm IPSSSSo scales well on a variety of multi-core machines. We outperform our closest in-place competitor by a factor of up to 3. Even as a sequential algorithm, we are up to 1.5 times faster than the closest sequential competitor, BlockQuicksort.

BibTeX - Entry

  author =	{Michael Axtmann and Sascha Witt and Daniel Ferizovic and Peter Sanders},
  title =	{{In-Place Parallel Super Scalar Samplesort (IPSSSSo)}},
  booktitle =	{25th Annual European Symposium on Algorithms (ESA 2017)},
  pages =	{9:1--9:14},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-049-1},
  ISSN =	{1868-8969},
  year =	{2017},
  volume =	{87},
  editor =	{Kirk Pruhs and Christian Sohler},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{},
  URN =		{urn:nbn:de:0030-drops-78542},
  doi =		{10.4230/LIPIcs.ESA.2017.9},
  annote =	{Keywords: shared memory, parallel sorting, in-place algorithm, comparison-based sorting, branch prediction}

Keywords: shared memory, parallel sorting, in-place algorithm, comparison-based sorting, branch prediction
Collection: 25th Annual European Symposium on Algorithms (ESA 2017)
Issue Date: 2017
Date of publication: 01.09.2017

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