Parallel Lightweight Wavelet Tree, Suffix Array and FM-Index Construction

Labeit, Julian; Shun, Julian; Blelloch, Guy E.

doi:10.1109/dcc.2016.117

Cited by 30 publications

(21 citation statements)

References 25 publications

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“…• Simultaneously, our new algorithms use much less space than all previous ones: on realistically sized alphabets, pcWT uses almost no space in addition to the input and output, while psWT uses only one additional array of the same size as the text. Previous ones such as serialWT or recWT [13] use at least twice as much additional space.…”

Section: Our Contributionsmentioning

confidence: 99%

“…Fuentes-Sepúlveda et al [7] were the first to describe and implement practical parallel WT-construction algorithms, requiring O(n) time and O(n lg σ) work. Faster practical approaches were presented subsequently by Shun [20] and by Labeit et al [13], both requiring O(lg n lg σ) time and O(n lg σ) work.…”

Section: Further Related Workmentioning

confidence: 99%

“…The domain decomposition [7,13] is a popular technique for parallel WT-construction. There, each core gets a slice of the text of size Θ( n p ) and computes a partial WT for that slice (in parallel).…”

Section: Domain Decompositionmentioning

confidence: 99%

“…It requires the same space as a WT and has the same asymptotic running times for access, rank, and select; but in practice it is often faster than a WT for rank and select queries [2], as it needs less calls to binary rank/select data structures. However, the fact that the WM loses some nice structural properties of the WT makes it harder to parallelize its construction, as divide-and-conquer WT-construction algorithms, e. g. recWT [13], cannot simply be transformed to WMs.…”

Section: The Wavelet Matrixmentioning

confidence: 99%

See 3 more Smart Citations

Simple, Fast and Lightweight Parallel Wavelet Tree Construction

Fischer

Kurpicz

Löbel

2018

2018 Proceedings of the Twentieth Workshop on Algorithm Engineering and Experiments (ALENEX)

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The wavelet tree (Grossi et al. [SODA, 2003]) and wavelet matrix (Claude et al. [Inf. Syst., 47:15-32, 2015]) are compact indices for texts over an alphabet [0, σ) that support rank, select and access queries in O(lg σ) time. We first present new practical sequential and parallel algorithms for wavelet tree construction. Their unifying characteristics is that they construct the wavelet tree bottom-up, i. e., they compute the last level first. We also show that this bottom-up construction can easily be adapted to wavelet matrices. In practice, our best sequential algorithm is up to twice as fast as the currently fastest sequential wavelet tree construction algorithm (Shun [DCC, 2015]), simultaneously saving a factor of 2 in space. This scales up to 32 cores, where we are about equally fast as the currently fastest parallel wavelet tree construction algorithm (Labeit et al. [DCC, 2016]), but still use only about 75 % of the space. An additional theoretical result shows how to adapt any wavelet tree construction algorithm to the wavelet matrix in the same (asymptotic) time, using only little extra space.

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Section: Our Contributionsmentioning

confidence: 99%

Section: Further Related Workmentioning

confidence: 99%

Section: Domain Decompositionmentioning

confidence: 99%

Section: The Wavelet Matrixmentioning

confidence: 99%

See 2 more Smart Citations

Simple, Fast and Lightweight Parallel Wavelet Tree Construction

Fischer

Kurpicz

Löbel

2018

2018 Proceedings of the Twentieth Workshop on Algorithm Engineering and Experiments (ALENEX)

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“…They are shown in Table III. As expected, the construction is significantly faster for BFSI-SAM (as fewer items are inserted into a Bloom filter) and may even reach above 100 MB/s, which, to our knowledge, far exceeds the performance of the best FM or suffix array construction algorithms [28][29][30] on a similar hardware.…”

Section: Resultsmentioning

confidence: 57%

A Bloom filter based semi‐index on q‐grams

2016

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Summary We present a simple q‐gram based semi‐index, which allows to look for a pattern typically only in a small fraction of text blocks. Several space‐time tradeoffs are presented. Experiments on Pizza & Chili datasets show that our solution is up to three orders of magnitude faster than the Claude et al. (Journal of Discrete Algorithms 2012; 11:37) semi‐index at a comparable space usage. Moreover, the construction of our data structure is fast and easily parallelizable. Copyright © 2016 John Wiley & Sons, Ltd.

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Fast and Compact Planar Embeddings

Ferres

Fuentes

Gagie

et al. 2017

Lecture Notes in Computer Science

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There are many representations of planar graphs, but few are as elegant as Turán's (1984): it is simple and practical, uses only 4 bits per edge, can handle self-loops and multi-edges, and can store any specified embedding. Its main disadvantage has been that "it does not allow efficient searching" (Jacobson, 1989). In this paper we show how to add a sublinear number of bits to Turán's representation such that it supports fast navigation while retaining simplicity. As a consequence of the inherited simplicity, we offer the first efficient parallel construction of a compact encoding of a planar graph embedding. Our experimental results show that the resulting representation uses about 6 bits per edge in practice, supports basic navigation operations within a few microseconds, and can be built sequentially at a rate below 1 microsecond per edge, featuring a linear speedup with a parallel efficiency around 50% for large datasets.

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Parallel Lightweight Wavelet Tree, Suffix Array and FM-Index Construction

Cited by 30 publications

References 25 publications

Simple, Fast and Lightweight Parallel Wavelet Tree Construction

Simple, Fast and Lightweight Parallel Wavelet Tree Construction

A Bloom filter based semi‐index on q‐grams

Fast and Compact Planar Embeddings

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