Efficient Non-Quadratic Quick Sort (NQQuickSort)

Daoud, Amjad M.; Abdel-Jaber, Hussein; Ababneh, Jafar

doi:10.1007/978-3-642-22603-8_58

Cited by 4 publications

(4 citation statements)

References 13 publications

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“…If exceeded, then we switch to heapsort, which we find to be "only" 20-40 times as expensive as vectorized quicksort (Table 2). By contrast, binary quicksort or pivot switching 6,20 may recurse up to 64 times for 64-bit inputs, or even 1024 times (log 2 of the maximum double-precision floating-point exponent).…”

Section: Pivot Selectionmentioning

confidence: 99%

Vectorized and performance‐portable quicksort

et al. 2022

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Recent works showed that implementations of quicksort using vector CPU instructions can outperform the non‐vectorized algorithms in widespread use. However, these implementations are typically single‐threaded, implemented for a particular instruction set, and restricted to a small set of key types. We lift these three restrictions: our proposed vqsort algorithm integrates into the state‐of‐the‐art parallel sorter ips4o$$ ip{s}^4o $$, with a geometric mean speedup of 1.59. The same implementation works on seven instruction sets (including SVE and RISC‐V V) across four platforms. It also supports floating‐point and 16–128 bit integer keys. To the best of our knowledge, this is the fastest sort for large arrays of non‐tuple keys on CPUs, up to 20 times as fast as the sorting algorithms implemented in standard libraries. This article focuses on the practical engineering aspects enabling the speed and portability, which we have not yet seen demonstrated for a quicksort implementation. Furthermore, we introduce compact and transpose‐free sorting networks for in‐register sorting of small arrays, and a vector‐friendly pivot sampling strategy that is robust against adversarial input.

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Section: Pivot Selectionmentioning

confidence: 99%

Vectorized and performance‐portable quicksort

et al. 2022

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“…If exceeded, then we switch to Heapsort, which we find to be "only" 20-40 times as expensive as vectorized Quicksort (Table 2). By contrast, binary Quicksort or pivot switching [20,6] may recurse up to 64 times for 64-bit inputs, or even 1024 times (log 2 of the maximum double-precision floating-point exponent).…”

Section: Pivot Selectionmentioning

confidence: 99%

Vectorized and performance-portable Quicksort

Blacher¹,

Giesen²,

Sanders³

et al. 2022

Preprint

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Recent works showed that implementations of Quicksort using vector CPU instructions can outperform the non-vectorized algorithms in widespread use. However, these implementations are typically singlethreaded, implemented for a particular instruction set, and restricted to a small set of key types. We lift these three restrictions: our proposed vqsort algorithm integrates into the state-of-the-art parallel sorter ips4o, with a geometric mean speedup of 1.59. The same implementation works on seven instruction sets (including SVE and RISC-V V) across four platforms. It also supports floating-point and 16-128 bit integer keys. To the best of our knowledge, this is the fastest sort for non-tuple keys on CPUs, up to 20 times as fast as the sorting algorithms implemented in standard libraries. This paper focuses on the practical engineering aspects enabling the speed and portability, which we have not yet seen demonstrated for a Quicksort implementation. Furthermore, we introduce compact and transpose-free sorting networks for in-register sorting of small arrays, and a vector-friendly pivot sampling strategy that is robust against adversarial input.

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“…The research on improvements for pivot procedure results in the new mechanisms of changing elements in the output string. In the article of Daoud et al [15] an interesting mechanism for nonquadratic method was proposed, and in the work of Edmondson [16] the research on various pivot possibilities was discussed, while in the paper by Kushagra et al [17] a multipivot procedure was proposed.…”

Section: Introductionmentioning

confidence: 99%

Fully Flexible Parallel Merge Sort for Multicore Architectures

2018

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The development in multicore architectures gives a new line of processors that can flexibly distribute tasks between their logical cores. These need flexible models of efficient algorithms, both fast and stable. A new line of efficient sorting algorithms can support these systems to efficiently use all available resources. Processes and calculations shall be flexibly distributed between cores to make the performance as high as possible. In this article we present a fully flexible sorting method designed for parallel processing. The idea we describe in this article is based on modified merge sort, which in parallel form is designed for multicore architectures. The novelty of this idea is in particular way of processing. We have developed a fully flexible method that can be implemented for a number of processors. The tasks are flexibly distributed between logical cores to increase the efficiency of sorting. The method preserves separation of concerns; therefore, each of the processors works separately without any cross actions and interruptions. The proposed method was described in theoretical way, examined in tests, and compared to other methods. The results confirm high efficiency and show that with each newly added processor sorting becomes faster and more efficient.

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Efficient Non-Quadratic Quick Sort (NQQuickSort)

Cited by 4 publications

References 13 publications

Vectorized and performance‐portable quicksort

Vectorized and performance‐portable quicksort

Vectorized and performance-portable Quicksort

Fully Flexible Parallel Merge Sort for Multicore Architectures

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