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42nd International Conference on Computer Design

作者： Liu, Rui Zhang, Xiaoyu Wang, Xinyu Min, Feng Luo, Zhejian Chen, Xiaoming Han, Yinhe Tang, Minghua Xiangtan Univ Sch Mat Sci & Engn Xiangtan Peoples R China Chinese Acad Sci Inst Comp Technol Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9798350380415;9798350380408

sorting is one of the most fundamental operations in computer programming and used in countless algorithms. The performance of traditional von Neumann computers running sorting is limited by the bandwidth between memories and processors. Computing-in-memory (CiM) is a promising technology which has the potential to solve the "memory wall" bottleneck. CiM is suitable for data-intensive applications, and it is ideal for accelerating large-scale data sorting. In this paper, we propose a novel in-memory sorting accelerator, named MemSort, based on a proposed in-memory comparison array design based on emerging non-volatile devices. MemSort supports three sort operations including counting sort, merging sort, and the combination of counting sort and merging sort. We build a performance model for the combination sort which enables flexible allocation of resources under given constraints to meet the requirements of various applications for sorting. The evaluation results show that MemSort shows significant performance improvement and energy efficiency at both the system level and application level when processing large-scale data sorting. Compared with the CPU implementation, MemSort achieves energy savings of 19.69-72.75x and speedups of 24.48-38.58x with the same power constraint. MemSort's throughput is at least 4.86x higher than that of the recent FPGA-based sorting accelerator FANS. MemSort exhibits more than 11x throughput and 4.03x area efficiency, compared with the recent CiM-based sorting accelerator, RIME.

关键词： Computing-in-memory Non-volatile memory sorting algorithm

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Research of efficiency optimization in tobacco automated sorting system

Research of efficiency optimization in tobacco automated sor...

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IEEE International Conference on Automation and Logistics

作者： Zhang, Yigong Wu, Yaohua Shandong Univ Logist Inst Jinan 250100 Shandong Peoples R China

ISBN: (纸本)9781424415304

This paper presents a method of analyzing efficiency of tobacco automated sorting system and two optimal methods based on the results of analysis. In efficiency analysis, we create a definition-efficiency bottleneck as the criterion of efficiency judgement. Then based on bottleneck, we devise 2 kinds of optimal methods. One mends the system's sorting algorithm to fit the bottleneck;the other eliminates the bottleneck radically by changing the layout of sorting system. The first method improves system's sorting efficiency from 7500/h to 9500/h, while the second method can improve system's sorting efficiency above 12000/h.

关键词： efficiency bottleneck sorting algorithm layout

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Redesigning the sorting Engine for Persistent Memory 26th

Redesigning the Sorting Engine for Persistent Memory

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26th International Conference on Database Systems for Advanced Applications (DASFAA)

作者： Hua, Yifan Huang, Kaixin Zheng, Shengan Huang, Linpeng Shanghai Jiao Tong Univ Shanghai Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9783030731991;9783030732004

Emerging persistent memory (PM, also termed as nonvolatile memory) technologies can promise large capacity, non-volatility, byte-addressability and DRAM-comparable access latency. Such amazing features have inspired a host of PM-based storage systems and applications that store and access data directly in PM. sorting is an important function for many systems, but how to optimize sorting for PM-based systems has not been systematically studied yet. In this paper, we conduct extensive experiments for many existing sorting methods, including both conventional sorting algorithms adapted for PM and recently-proposed PM-friendly sorting techniques, on a real PM platform. The results indicate that these sorting methods all have drawbacks for various workloads. Some of the results are even counterintuitive compared to running on a DRAM-simulated platform in their papers. To the best of our knowledge, we are the first to perform a systematic study on the sorting issue for persistent memory. Based on our study, we propose an adaptive sorting engine, namely SmartSort, to optimize the sorting performance for different conditions. The experimental results demonstrate that SmartSort remarkably outperforms existing sorting methods in a variety of cases.

关键词： Persistent memory sorting algorithm Pointer-indirect Wear-leveling

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Parallel Divide-and-Conquer algorithms for Bubble Sort, Selection Sort and Insertion Sort

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COMPUTER JOURNAL 2022年第10期65卷 2709-2719页

作者： Ganapathi, Pramod Chowdhury, Rezaul SUNY Stony Brook Stony Brook NY 11794 USA

We present efficient parallel recursive divide-and-conquer algorithms for bubble sort, selection sort, and insertion sort. Our algorithms have excellent data locality and are highly parallel. The computational complex... 详细信息

关键词： parallel divide-and-conquer sorting algorithm bubble sort selection sort insertion sort merge sort quicksort

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Linear-time suffix sorting - A new approach for suffix array construction 27

Linear-time suffix sorting - A new approach for suffix array...

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27th Annual Symposium on Combinatorial Pattern Matching, CPM 2016

作者： Baier, Uwe Institute of Theoretical Computer Science Ulm University UlmD-89069 Germany

ISBN: (纸本)9783959770125

This paper presents a new approach for linear-time suffix sorting. It introduces a new sorting principle that can be used to build the first non-recursive linear-time suffix array construction algorithm named GSACA. Although GSACA cannot keep up with the performance of state of the art suffix array construction algorithms, the algorithm introduces a couple of new ideas for suffix array construction, and therefore can be seen as an 'idea collection' for further suffix array construction improvements. © Uwe Baier.

关键词： Linear-time sorting algorithm Suffix array

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A classification algorithm for selective dynamical downscaling of precipitation extremes

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HYDROLOGY AND EARTH SYSTEM SCIENCES 2018年第8期22卷 4183-4200页

作者： Meredith, Edmund P. Rust, Henning W. Ulbrich, Uwe Free Univ Berlin Inst Meteorol Carl Heinrich Becker Weg 6-10 D-12165 Berlin Germany

High-resolution climate data O(1 km) at the catchment scale can be of great value to both hydrological modellers and end users, in particular for the study of extreme precipitation. While dynamical downscaling with convection-permitting models is a valuable approach for producing quality high-resolution O(1 km) data, its added value can often not be realized due to the prohibitive computational expense. Here we present a novel and flexible classification algorithm for discriminating between days with an elevated potential for extreme precipitation over a catchment and days without, so that dynamical downscaling to convection-permitting resolution can be selectively performed on high-risk days only, drastically reducing total computational expense compared to continuous simulations;the classification method can be applied to climate model data or reanalyses. Using observed precipitation and the corresponding synoptic-scale circulation patterns from reanalysis, characteristic extremal circulation patterns are identified for the catchment via a clustering algorithm. These extremal patterns serve as references against which days can be classified as potentially extreme, subject to additional tests of relevant meteorological predictors in the vicinity of the catchment. Applying the classification algorithm to reanalysis, the set of potential extreme days (PEDs) contains well below 10 % of all days, though it includes essentially all extreme days;applying the algorithm to reanalysis-driven regional climate simulations over Europe (12 km resolution) shows similar performance, and the subsequently dynamically downscaled simulations (2 km resolution) well reproduce the observed precipitation statistics of the PEDs from the training period. Additional tests on continuous 12 km resolution historical and future (RCP8.5) climate simulations, downscaled in 2 km resolution time slices, show the algorithm again reducing the number of days to simulate by over 90 % and performing con

关键词： circulation patterns Extreme Precipitation sorting algorithm Catchment area extremum values simulating Resolving power

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Rule-Based Classification Models of Molecular Autofluorescence

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JOURNAL OF CHEMICAL INFORMATION AND MODELING 2015年第2期55卷 434-445页

作者： Su, Bo-Han Tu, Yi-Shu Lin, Olivia A. Harn, Yeu-Chern Shen, Meng-Yu Tseng, Yufeng J. Natl Taiwan Univ Dept Comp Sci & Informat Engn Taipei 106 Taiwan Natl Taiwan Univ Grad Inst Biomed Elect & Bioinformat Taipei 106 Taiwan Natl Taiwan Univ Sch Med Drug Res Ctr Taipei 106 Taiwan

Fluorescence-based detection has been commonly used in high-throughput screening (HTS) assays. Autofluorescent compounds, which can emit light in the absence of artificial fluorescent markers, often interfere with the detection of fluorophores and result in false positive signals in these assays. This interference presents a major issue in fluorescence-based screening techniques. In an effort to reduce the time and cost that will be spent on prescreening of autofluorescent compounds, in silico autofluorescence prediction models were developed for selected fluorescence-based assays in this study. Five prediction models were developed based on the respective fluorophores used in these HTS assays, which absorb and emit light at specific wavelengths (excitation/emission): Alexa Fluor 350 (A350) (340 nm/450 nm), 7-amino-4-trifluoromethyl-coumarin (AFC) (405 nm/520 nm), Alexa Fluor 488 (A488) (480 nm/540 nm), Rhodamine (547 nm/598 nm), and Texas Red (547 nm/618 nm). The C5.0 rule-based classification algorithm and PubChem 2D chemical structure fingerprints were used to develop prediction models. To optimize the accuracies of these prediction models despite the highly imbalanced ratio of fluorescent versus nonfluorescent compounds presented in the collected data sets, oversampling and undersampling strategies were applied. The average final accuracy achieved for the training set was 97%, and that for the testing set was 92%. In addition, five external data sets were used to further validate the models. Ultimately, 14 representative structural features (or rules) were determined to efficiently predict autofluorescence in data sets containing both fluorescent and nonfluorescent compounds. Several cases were illustrated in this study to demonstrate the applicability of these rules.

关键词： sorting algorithm Newton meter fluor Fluorophores Texas red Rule Data Collection Methods Dataset Prediction models Alexa Fluorescence prescreening TEST SETS Oversampling autofluorescence

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Design patterns for sorting 01

Design patterns for sorting

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32nd SIGCSE Technical Symposium on Computer Science Education

作者： Nguyen, D Wong, SB Rice Univ Dept Comp Sci Houston TX 77251 USA

ISBN: (纸本)1581133294

Drawing on Merritt's divide-and-conquer sorting taxonomy [1], we model comparison-based sorting as an abstract class with a template method to perform the sort by relegating the splitting and joining of arrays to its concrete subclasses. Comparison on objects is carried out via an abstract ordering strategy. This reduces code complexity and simplifies the analyses of the various concrete sorting algorithms. Performance measurements and visualizations can be added without modifying any code by utilizing the decorator design pattern. This object-oriented design not only provides the student a concrete way of unifying seemingly disparate sorting algorithms but also help him/her differentiate them at the proper level of abstraction.

关键词： design pattern sorting programs sorting algorithm template process grade level of abstraction Visualization Concrete drawing Drawing encoding complexity ordering strategy Painters

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Variable selection with a support vector machine for discriminating Cryptococcus fungal species based on ATR-FTIR spectroscopy

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ANALYTICAL METHODS 2017年第20期9卷 2964-2970页

作者： Morais, Camilo L. M. Costa, Fernanda S. L. Lima, Kassio M. G. Univ Fed Rio Grande do Norte Inst Chem Biol Chem & Chemometr BR-59072970 Natal RN Brazil

Variable selection with supervised classification is currently an important tool for discriminating biological samples. In this paper, 15 supervised classification algorithms based on a support vector machine (SVM) were applied to discriminate Cryptococcus neoformans and Cryptococcus gattii fungal species using ATR-FTIR spectroscopy. These two fungal species of the Cryptococcus genus are the etiological agents of Cryptococcosis, which is an opportunistic or primary fungal infection with global distribution. This disease is potentially fatal, especially for immunocompromised patients, like those suffering from AIDS. The multivariate classification algorithms tested were based on principal component analysis (PCA), successive projections algorithm (SPA) and genetic algorithm (GA) as data reduction and variable selection methods, being coupled to a SVM with different kernel functions (linear, quadratic, 3rd order polynomial, radial basis function, and multilayer perceptron). Some of these algorithms achieved very successful classification rates for discriminating fungal species, with accuracy, sensitivity, and specificity equal to 100% using both SPA-SVM-polynomial and GA-SVM-polynomial algorithms. These results show the potential of such techniques coupled to ATR-FTIR spectroscopy as a rapid and non-destructive tool for classifying these fungal species.

关键词： Genetic algorithm (GA) Cryptococcosis Spectrography Cryptococcus gattii algorithms Immunocompromised Host Fourier transform infrared spectroscopy Fungi Multilayer perceptrons Support Vector Network Input variables sorting algorithm

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Robust Doppler classification technique based on hidden Markov models

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IEE PROCEEDINGS-RADAR SONAR AND NAVIGATION 2003年第1期150卷 33-36页

作者： Jahangir, M Ponting, KM O'Loghlen, JW QinetiQ Ltd Malvern WR14 3PS Worcs England 20 20 Speech Ltd Malvern WR14 3SZ Worcs England

A classification algorithm is presented that uses hidden Markov models (HMMs) to carry out recognition between three classes of targets: personnel, tracked vehicles and wheeled vehicles. It exploits the time-varying nature of radar Doppler data in a manner similar to techniques used in speech recognition, albeit with a modified topology, to distinguish targets with different Doppler characteristics. The algorithm was trained and tested on real radar signatures of multiple examples of moving targets from each class, and the performance was shown to be invariant to target speed and orientation and was able to be generalised with respect to variants within a class.

关键词： vehicle sorting algorithm variants time varying Targets Doppler effect Speech recognition radar signatures Invariant tracked vehicles Hidden Markov models Doppler radar

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