An efficient parallelization of longest prefix match and application on data compression

Özsoy, Adnan

doi:10.1177/1094342015621367

Cited by 3 publications

(1 citation statement)

References 36 publications

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“…Vector data tiling can be applied to reduce data extent to enable value converting to an even smaller byte integer, with which a data rate saving of 80% can be achieved [20]. Lossless compression algorithms are mainly based on information theory, such as Huffman encoding, LZ series encoding [21,22], which are based on data redundancy evaluation and dictionary building. This kind of algorithm can achieve error-free decoding and is mostly used for file compression and data archiving, which can be applied in data compression of vector file or internet transmission.…”

Section: Introductionmentioning

confidence: 99%

A Multiresolution Vector Data Compression Algorithm Based on Space Division

Liu

Wang

et al. 2020

IJGI

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Vector data compression can significantly improve efficiency of geospatial data management, visualization and data transmission over internet. Existing compression methods are either based on information theory for lossless compression mainly or based on map generalization methods for lossy compression. Coordinate values of vector spatial data are mostly represented using floating-point type in which data redundancy is small and compression ratio using lossy algorithms is generally better than that of lossless compression algorithms. The purpose of paper is to implement a new algorithm for efficient compression of vector data. The algorithm, named space division based compression (SDC), employs the basic idea of linear Morton and Geohash encoding to convert floating-point type values to strings of binary chain with flexible accuracy level. Morton encoding performs multiresolution regular spatial division to geographic space. Each level of regular grid splits space horizontally and vertically. Row and column numbers in binary forms are bit interleaved to generate one integer representing the location of each grid cell. The integer values of adjacent grid cells are proximal to each other on one dimension. The algorithm can set the number of divisions according to accuracy requirements. Higher accuracy can be achieved with more levels of divisions. In this way, multiresolution vector data compression can be achieved accordingly. The compression efficiency is further improved by grid filtering and binary offset for linear and point geometries. The vector spatial data compression takes visual lossless distance on screen display as accuracy requirement. Experiments and comparisons with available algorithms show that this algorithm produces a higher data rate saving and is more adaptable to different application scenarios.

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Section: Introductionmentioning

confidence: 99%

A Multiresolution Vector Data Compression Algorithm Based on Space Division

Liu

Wang

et al. 2020

IJGI

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Fast Longest Prefix Matching by Exploiting SIMD Instructions

et al. 2020

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Longest prefix matching (LPM) is a fundamental process in IP routing used not only in traditional hardware routers but also in software middleboxes. However, the performance of LPM in software is still insufficient for processing packets at over 100 Gbps, although previous studies have tackled this issue by exploiting the CPU cache or accelerators such as GPUs. To improve the performance of software LPM further, we propose a novel LPM method called Spider, which exploits a single-instruction multiple-data (SIMD) mechanism in the CPU. Spider achieves performing LPM for up to 16 destination IP address in parallel by a routing table structure carefully designed for processing by the SIMD instructions. We evaluated Spider from the following three perspectives: the improvement of LPM performance derived from the parallelism provided by the SIMD mechanism, performance comparison with other methods, and performance scalability. The evaluation shows that Spider dramatically improves the LPM performance, which reaches 1.8-3.2 times compared with the state-of-the-art methods. Moreover, Spider achieves 5,074 million lookups per second with 16 CPU cores, which is equivalent to the processing capacity of 3.4 Tbps in short packets; the performance opens up the possibility of packet processing at the terabit-class rate by software.INDEX TERMS IP routing, longest prefix matching (LPM), single-instruction multiple-data (SIMD), software middlebox.

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An Efficient Match Search Approach Using Two-Dimensional Hash Function in Hardware-Based Dictionary Compression

Dong

2019

J CIRCUIT SYST COMP

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The hardware-based dictionary compression is widely adopted for high speed requirement of real-time data processing. Hash function helps to manage large dictionary to improve compression ratio but is prone to collisions, so some phrases in match search result are not true matches. This paper presents a novel match search approach called dual chaining hash refining, which can improve the efficiency of match search. From the experimental results, our method showed obvious advantage in compression speed compared with other approach that utilizes single hash function described in the previous publications.

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An efficient parallelization of longest prefix match and application on data compression

Cited by 3 publications

References 36 publications

A Multiresolution Vector Data Compression Algorithm Based on Space Division

A Multiresolution Vector Data Compression Algorithm Based on Space Division

Fast Longest Prefix Matching by Exploiting SIMD Instructions

An Efficient Match Search Approach Using Two-Dimensional Hash Function in Hardware-Based Dictionary Compression

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