An external sorting algorithm

Cybernetics ◽  
1968 ◽  
Vol 2 (5) ◽  
pp. 75-79
Author(s):  
V. A. Litvinov
Keyword(s):  
Sorting Algorithm ◽  
External Sorting

scholarly journals BioSeqZip: a collapser of NGS redundant reads for the optimization of sequence analysis

2020 ◽  
Vol 36 (9) ◽  
pp. 2705-2711 ◽  
Author(s):  
Gianvito Urgese ◽  
Emanuele Parisi ◽  
Orazio Scicolone ◽  
Santa Di Cataldo ◽  
Elisa Ficarra
Keyword(s):  
Sequence Analysis ◽  
Supplementary Information ◽  
Sorting Algorithm ◽  
Rna Seq ◽  
Compact Sets ◽  
Analysis Pipeline ◽  
Alignment Algorithms ◽  
External Sorting ◽  
Computational Resources ◽  
Generation Sequencing

Abstract Motivation High-throughput next-generation sequencing can generate huge sequence files, whose analysis requires alignment algorithms that are typically very demanding in terms of memory and computational resources. This is a significant issue, especially for machines with limited hardware capabilities. As the redundancy of the sequences typically increases with coverage, collapsing such files into compact sets of non-redundant reads has the 2-fold advantage of reducing file size and speeding-up the alignment, avoiding to map the same sequence multiple times. Method BioSeqZip generates compact and sorted lists of alignment-ready non-redundant sequences, keeping track of their occurrences in the raw files as well as of their quality score information. By exploiting a memory-constrained external sorting algorithm, it can be executed on either single- or multi-sample datasets even on computers with medium computational capabilities. On request, it can even re-expand the compacted files to their original state. Results Our extensive experiments on RNA-Seq data show that BioSeqZip considerably brings down the computational costs of a standard sequence analysis pipeline, with particular benefits for the alignment procedures that typically have the highest requirements in terms of memory and execution time. In our tests, BioSeqZip was able to compact 2.7 billion of reads into 963 million of unique tags reducing the size of sequence files up to 70% and speeding-up the alignment by 50% at least. Availability and implementation BioSeqZip is available at https://github.com/bioinformatics-polito/BioSeqZip. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Fringe analysis for Extquick: Anin situ distributive external sorting algorithm

1991 ◽  
Vol 92 (2) ◽  
pp. 141-160 ◽  
Author(s):  
Walter Cunto ◽  
Gasto´n H. Gonnet ◽  
J. Ian Munro ◽  
Patricio V. Poblete
Keyword(s):  
Sorting Algorithm ◽  
Fringe Analysis ◽  
External Sorting

scholarly journals External Sorting Algorithm: State-of-the-Art and Future Directions

2020 ◽  
Vol 806 ◽  
pp. 012040
Author(s):  
Wenhan Chen ◽  
Yang Liu ◽  
Zhiguang Chen ◽  
Fang Liu ◽  
Nong Xiao
Keyword(s):  
State Of The Art ◽  
Sorting Algorithm ◽  
Future Directions ◽  
External Sorting

A new external sorting algorithm with no additional disk space

2003 ◽  
Vol 86 (5) ◽  
pp. 229-233 ◽  
Author(s):  
Rafiqul Islam ◽  
Nasim Adnan ◽  
Nur Islam ◽  
Shohorab Hossen
Keyword(s):  
Sorting Algorithm ◽  
Disk Space ◽  
External Sorting

scholarly journals Performance Analysis of a Faster In−place External Sorting Algorithm

2020 ◽  
pp. 1-7
Author(s):  
Asaduzzaman Nur Shuvo ◽  
Apurba Adhikary ◽  
Md. Bipul Hossain ◽  
Sultana Jahan Soheli
Keyword(s):  
Time Complexity ◽  
Divide And Conquer ◽  
Sorting Algorithm ◽  
Data Sets ◽  
Data Set ◽  
Internal Memory ◽  
Huge Data ◽  
External Sorting ◽  
Bottle Neck ◽  
Quick Sort

Data sets in large applications are often too gigantic to fit completely inside the computer’s internal memory. The resulting input/output communication (or I/O) between fast internal memory and slower external memory (such as disks) can be a major performance bottle−neck. While applying sorting on this huge data set, it is essential to do external sorting. This paper is concerned with a new in−place external sorting algorithm. Our proposed algorithm uses the concept of Quick−Sort and Divide−and−Conquer approaches resulting in a faster sorting algorithm avoiding any additional disk space. In addition, we showed that the average time complexity can be reduced compared to the existing external sorting approaches.

MONTRES : Merge ON-the-Run External Sorting Algorithm for Large Data Volumes on SSD Based Storage Systems

2017 ◽  
Vol 66 (10) ◽  
pp. 1689-1702 ◽  
Author(s):  
Arezki Laga ◽  
Jalil Boukhobza ◽  
Frank Singhoff ◽  
Michel Koskas
Keyword(s):  
Storage Systems ◽  
Large Data ◽  
Sorting Algorithm ◽  
External Sorting

An efficient external sorting algorithm

2000 ◽  
Vol 75 (4) ◽  
pp. 159-163 ◽  
Author(s):  
Fang-Cheng Leu ◽  
Yin-Te Tsai ◽  
Chuan Yi Tang
Keyword(s):  
Sorting Algorithm ◽  
External Sorting

Efficient External Sorting for Memory-Constrained Embedded Devices with Flash Memory

2021 ◽  
Vol 20 (4) ◽  
pp. 1-21
Author(s):  
Riley Jackson ◽  
Jonathan Gresl ◽  
Ramon Lawrence
Keyword(s):  
Data Collection ◽  
Flash Memory ◽  
Health And Safety ◽  
Sorting Algorithm ◽  
Output Buffer ◽  
Embedded Devices ◽  
Sorting Data ◽  
Limited Application ◽  
External Sorting ◽  
Merge Sort

Embedded devices are ubiquitous in areas of industrial and environmental monitoring, health and safety, and consumer appliances. A common use case is data collection, processing, and performing actions based on data analysis. Although many Internet of Things (IoT) applications use the embedded device simply for data collection, there are benefits to having more data processing done closer to data collection to reduce network transmissions and power usage and provide faster response. This work implements and evaluates algorithms for sorting data on embedded devices with specific focus on the smallest memory devices. In devices with less than 4 KB of available RAM, the standard external merge sort algorithm has limited application as it requires a minimum of three memory buffers and is not flash-aware. The contribution is a memory-optimized external sorting algorithm called no output buffer sort (NOBsort) that reduces the minimum memory required for sorting, has excellent performance for sorted or near-sorted data, and sorts on external memory such as SD cards or raw flash chips. When sorting large datasets, no output buffer sort reduces I/O and execution time by between 20% to 35% compared to standard external merge sort.

An Adaptive Sorting Algorithm for Almost Sorted List

2017 ◽  
Vol 5 (12) ◽  
pp. 169-172
Author(s):  
Rina Damdoo ◽  
◽  
◽  
Kanak Kalyani
Keyword(s):  
Sorting Algorithm ◽  
Adaptive Sorting
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