How Good Is Multi-Pivot Quicksort?

Multi-Pivot Quicksort refers to variants of classical quicksort where in the partitioning step $k$ pivots are used to split the input into $k + 1$ segments. For many years, multi-pivot quicksort was regarded as impractical, but in 2009 a 2-pivot approach by Yaroslavskiy, Bentley, and Bloch was chosen as the standard sorting algorithm in Sun's Java 7. In 2014 at ALENEX, Kushagra et al. introduced an even faster algorithm that uses three pivots. This paper studies what possible advantages multi-pivot quicksort might offer in general. The contributions are as follows: Natural comparison-optimal algorithms for multi-pivot quicksort are devised and analyzed. The analysis shows that the benefits of using multiple pivots with respect to the average comparison count are marginal and these strategies are inferior to simpler strategies such as the well known median-of-$k$ approach. A substantial part of the partitioning cost is caused by rearranging elements. A rigorous analysis of an algorithm for rearranging elements in the partitioning step is carried out, observing mainly how often array cells are accessed during partitioning. The algorithm behaves best if 3 to 5 pivots are used. Experiments show that this translates into good cache behavior and is closest to predicting observed running times of multi-pivot quicksort algorithms. Finally, it is studied how choosing pivots from a sample affects sorting cost. The study is theoretical in the sense that although the findings motivate design recommendations for multipivot quicksort algorithms that lead to running time improvements over known algorithms in an experimental setting, these improvements are small.Comment: Submitted to a journal, v2: Fixed statement of Gibb's inequality, v3: Revised version, especially improving on the experiments in Section

Simple and Fast BlockQuicksort using Lomuto's Partitioning Scheme

This paper presents simple variants of the BlockQuicksort 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 Hoare-based 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.Comment: Accepted at ALENEX 201

Dual-Pivot Quicksort: Optimality, Analysis and Zeros of Associated Lattice Paths

We present an average case analysis of a variant of dual-pivot quicksort. We show that the used algorithmic partitioning strategy is optimal, i.e., it minimizes the expected number of key comparisons. For the analysis, we calculate the expected number of comparisons exactly as well as asymptotically, in particular, we provide exact expressions for the linear, logarithmic, and constant terms. An essential step is the analysis of zeros of lattice paths in a certain probability model. Along the way a combinatorial identity is proven.Comment: This article supersedes arXiv:1602.0403

Multiway Powersort

We present a stable mergesort variant, Multiway Powersort, that exploits existing runs and finds nearly-optimal merging orders for k-way merges with negligible overhead. This builds on Powersort (Munro & Wild, ESA2018), which has recently replaced Timsort's suboptimal merge policy in the CPython reference implementation of Python, as well as in PyPy and further libraries. Multiway Powersort reduces the number of memory transfers, which increasingly determine the cost of internal sorting (as observed with Multiway Quicksort (Kushagra et al., ALENEX 2014; Aum\"uller & Dietzfelbinger, TALG 2016; Wild, PhD thesis 2016) and the inclusion of Dual-Pivot Quicksort in the Java runtime library). We demonstrate that our 4-way Powersort implementation can achieve substantial speedups over standard (2-way) Powersort and other stable sorting methods without compromising the optimally run-adaptive performance of Powersort

Manticore: Efficient Framework for Scalable Secure Multiparty Computation Protocols

We propose a novel MPC framework, Manticore, in the multiparty setting, with full threshold and semi-honest security model, supporting a combination of real number arithmetic (arithmetic shares), Boolean arithmetic (Boolean shares) and garbled circuits (Yao shares). In contrast to prior work [MZ17, MR18], Manticore never overflows, an important feature for machine learning applications. It achieves this without compromising efficiency or security. Compared to other overflow-free recent techniques such as MP-SPDZ [EGKRS20] that convert arithmetic to Boolean shares, we introduce a novel highly efficient modular lifting/truncation method that stays in the arithmetic domain. We revisit some of the basic MPC operations such as real-valued polynomial evaluation, division, logarithm, exponential and comparison by employing our modular lift in combination with existing efficient conversions between arithmetic, Boolean and Yao shares. Furthermore, we provide a highly efficient and scalable implementation supporting logistic regression models with real-world training data sizes and high numerical precision through PCA and blockwise variants (for memory and runtime optimizations). On a dataset of 50 million rows and 50 columns distributed among two players, it completes in one day with at least 10 decimal digits of precision.Our logistic regression solution placed first at Track 3 of the annual iDASH’2020 Competition. Finally, we mention a novel oblivious sorting algorithm built using Manticore