In-Place Parallel Super Scalar Samplesort (IPSSSSo)

Axtmann, Michael; Witt, Sascha; Ferizovic, Daniel; Sanders, Peter

doi:10.4230/lipics.esa.2017.9

Cited by 5 publications

(9 citation statements)

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“…Again, a lot of care is needed in the cleanup phase. IPS 4 o lends itself to parallelization, as demonstrated by the experiments in [4].…”

Section: Related Workmentioning

confidence: 98%

“…IPS 4 o [4] provides an in-place samplesort variant of [17] by combining it with the idea of using blocks from BlockQuicksort. Instead of two blocks, they use a large number of blocks, one for each…”

Section: Related Workmentioning

confidence: 99%

“…This means other tools are needed to reduce the performance penalty that these mispredictions incur, which can be as large as 15 cycles on a modern Intel i7 CPU [10], and even larger on other architectures [12]. As we will describe in Section 2, under certain circumstances modern processors can avoid branches using socalled conditional move-operations, among others, which were used in a number of implementations of quicksort variants [17,8,7,4]. More detailed information about these methods is deferred to the related work part of the introduction.…”

Section: Introductionmentioning

confidence: 99%

“…In the branch-free regime of [17,7,4], a lot of complexity has been introduced to the algorithm. In fact, Axtmann et al [4] describe code complexity as the main drawback of their approach, stat-ing "formal verification of the correctness of the implementation might help to increase trust in the remaining cases." [4, p. 12].…”

Section: Introductionmentioning

confidence: 99%

“…4 o IPS4 o with 2 ℓ pivots makes ℓ comparisons per element. Thus, it makes ℓ • n + O(1) comparisons in the partitioning step.…”

mentioning

confidence: 99%

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Simple and Fast BlockQuicksort using Lomuto's Partitioning Scheme

Aumüller

Hass²

2019

2019 Proceedings of the Twenty-First Workshop on Algorithm Engineering and Experiments (ALENEX)

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This paper presents simple variants of the Block-Quicksort algorithm described by Edelkamp and Weiss (ESA 2016). The simplification is achieved by using Lomuto's partitioning scheme instead of Hoare's crossing pointer technique to partition the input. To achieve a robust sorting algorithm that works well on many different input types, the paper introduces a novel two-pivot variant of Lomuto's partitioning scheme. A surprisingly simple twist to the generic two-pivot quicksort approach makes the algorithm robust. The paper provides an analysis of the theoretical properties of the proposed algorithms and compares them to their competitors. The analysis shows that Lomuto-based approaches incur a higher average sorting cost than the Hoarebased approach of BlockQuicksort. Moreover, the analysis is particularly useful to reason about pivot choices that suit the two-pivot approach. An extensive experimental study shows that, despite their worse theoretical behavior, the simpler variants perform as well as the original version of BlockQuicksort.

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“…Again, a lot of care is needed in the cleanup phase. IPS 4 o lends itself to parallelization, as demonstrated by the experiments in [4].…”

Section: Related Workmentioning

confidence: 98%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…4 o IPS4 o with 2 ℓ pivots makes ℓ comparisons per element. Thus, it makes ℓ • n + O(1) comparisons in the partitioning step.…”

mentioning

confidence: 99%

See 3 more Smart Citations

Simple and Fast BlockQuicksort using Lomuto's Partitioning Scheme

Aumüller

Hass²

2019

2019 Proceedings of the Twenty-First Workshop on Algorithm Engineering and Experiments (ALENEX)

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A Parallel Triple-Pivot Sorting (PTPSort) Algorithm: Preliminary Results

Rattanatranurak

Kittitornkun

2020

2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technolo

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Fast, parallel, and cache-friendly suffix array construction

Khan,

Rubel,

Molloy

et al. 2024

Algorithms Mol Biol

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Purpose String indexes such as the suffix array (sa) and the closely related longest common prefix (lcp) array are fundamental objects in bioinformatics and have a wide variety of applications. Despite their importance in practice, few scalable parallel algorithms for constructing these are known, and the existing algorithms can be highly non-trivial to implement and parallelize. Methods In this paper we present caps-sa, a simple and scalable parallel algorithm for constructing these string indexes inspired by samplesort and utilizing an LCP-informed mergesort. Due to its design, caps-sa has excellent memory-locality and thus incurs fewer cache misses and achieves strong performance on modern multicore systems with deep cache hierarchies. Results We show that despite its simple design, caps-sa outperforms existing state-of-the-art parallel sa and lcp-array construction algorithms on modern hardware. Finally, motivated by applications in modern aligners where the query strings have bounded lengths, we introduce the notion of a bounded-context sa and show that caps-sa can easily be extended to exploit this structure to obtain further speedups. We make our code publicly available at https://github.com/jamshed/CaPS-SA.

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In-Place Parallel Super Scalar Samplesort (IPSSSSo)

Cited by 5 publications

References 0 publications

Simple and Fast BlockQuicksort using Lomuto's Partitioning Scheme

Simple and Fast BlockQuicksort using Lomuto's Partitioning Scheme

A Parallel Triple-Pivot Sorting (PTPSort) Algorithm: Preliminary Results

Fast, parallel, and cache-friendly suffix array construction

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