B*-Sort: Enabling Write-Once Sorting for Nonvolatile Memory

Liang, Yuan‐Chang; Chen, Tseng-Yi; Chang, Yuan-Hao; Chen, Shuo-Han; Wei, Hsin-Wen; Shih, Wei-Kuan

doi:10.1109/tcad.2020.2979819

Cited by 8 publications

(3 citation statements)

References 29 publications

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“…In the literaure [6], the authors proposed a write-once sorting algorithm, named B*-sort. Differing from previous work, this work focused on pure NVM-based embedded system.…”

Section: Related Workmentioning

confidence: 99%

NVMSorting: Efficient Sorting on Non-Volatile Memory

Chu¹

2021

Proceedings of the 33rd International Conference on Software Engineering and Knowledge Engineering

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Non-volatile memory (NVM) as a new type of storage technology has many advantages such as non-volatility, byte addressability, high storage-density, and low energy consumption. Meanwhile, NVM has some limitations, e.g., asymmetric read and write latency, limited write endurance, and high price. Therefore, at present, it is not realistic to completely replace DRAM with NVM in computer systems. A more feasible scheme is to adopt the hybrid memory architecture composed of NVM and DRAM. Following the assumption of hybrid memory architecture, this paper proposes an NVM-friendly sorting algorithm called NVMSorting. Particularly, we introduce a new concept called natural runs to improve the existing MONTRES algorithm and present the cost analysis of the algorithm in the hybrid memory architecture. In order to verify the performance of our proposal, we implement six existing sorting algorithms as baselines, including the MONTRES algorithm, and conduct comparative experiments on an unsorted dataset and a partially sorted dataset. The experimental results suggest the efficiency of NVMsorting in terms of execution time and NVM writes. Especially, on the partially sorted dataset, NVMSorting has 6.2% improvement on time performance and 5.7% reduction on NVM writes compared to MONTRES, and 13.0% performance improvement and 27.1% NVM-write reduction compared to the traditional merge sorting algorithm.

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“…In the literaure [6], the authors proposed a write-once sorting algorithm, named B*-sort. Differing from previous work, this work focused on pure NVM-based embedded system.…”

Section: Related Workmentioning

confidence: 99%

NVMSorting: Efficient Sorting on Non-Volatile Memory

Chu¹

2021

Proceedings of the 33rd International Conference on Software Engineering and Knowledge Engineering

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“…Laga et al [ 27 ] proposed MONTRES, external sorting algorithm on SSDs to reduce the run time of sorting process by decreasing read and write costs of input and output operations and the size of temporary data. Liang et al [ 28 ] designed B*-sort, a new sorting algorithm, which can reduce the amount of write traffic on Nonvolatile random access memory (NVRAM). Moreover, the structure used in this algorithm is similar to the binary search tree, which takes the time complexity of O ( n log n ).…”

Section: Introductionmentioning

confidence: 99%

Fast continuous streaming sort in big streaming data environment under fixed-size single storage

2022

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Big streaming data environment concerns a complicated scenario where data to be processed continuously flow into a processing unit and certainly cause a memory overflow problem. This obstructs the adaptation of deploying all existing classic sorting algorithms because the data to be sorted must be entirely stored inside the fixed-size storage including the space in internal and external storage devices. Generally, it is always assumed that the size of each data chunk is not larger than the size of storage (M) but in fact the size of the entire stream (n) is usually much larger than M. In this paper, a new fast continuous streaming sorting is proposed to cope with the constraint of storage overflow. The algorithm was tested with various real data sets consisting of 10,000 to 17,000,000 numbers and different storage sizes ranging from 0.01n to 0.50n. It was found that the feasible lower bound of storage size is 0.35n with 100% sorting accuracy. The sorting time outperforms bubble sort, quick sort, insertion sort, and merge sort when data size is greater than 1,000,000 numbers. Remarkably, the sorting time of the proposed algorithm is 1,452 times less than the sorting time of external merge sort and 28.1767 times less than the sorting time of streaming data sort. The time complexity of proposed algorithm is O(n) while the space complexity is O(M).

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“…External sorting algorithms in the literature have focused on reducing the read and write cost in terms of the execution time for processing, but the storage issue for keeping big data is still ignored. Liang et al (2020) proposed a new algorithm, namely, B*-sort, which was designed on NVRAM and applied on a binary search tree structure.…”

Section: Introductionsmentioning

confidence: 99%

Correct and stable sorting for overflow streaming data with a limited storage size and a uniprocessor

Chaikhan¹,

Phimoltares²,

Lursinsap³

2021

PeerJ Computer Science

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Tremendous quantities of numeric data have been generated as streams in various cyber ecosystems. Sorting is one of the most fundamental operations to gain knowledge from data. However, due to size restrictions of data storage which includes storage inside and outside CPU with respect to the massive streaming data sources, data can obviously overflow the storage. Consequently, all classic sorting algorithms of the past are incapable of obtaining a correct sorted sequence because data to be sorted cannot be totally stored in the data storage. This paper proposes a new sorting algorithm called streaming data sort for streaming data on a uniprocessor constrained by a limited storage size and the correctness of the sorted order. Data continuously flow into the storage as consecutive chunks with chunk sizes less than the storage size. A theoretical analysis of the space bound and the time complexity is provided. The sorting time complexity is O (n), where n is the number of incoming data. The space complexity is O (M), where M is the storage size. The experimental results show that streaming data sort can handle a million permuted data by using a storage whose size is set as low as 35% of the data size. This proposed concept can be practically applied to various applications in different fields where the data always overflow the working storage and sorting process is needed.

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B*-Sort: Enabling Write-Once Sorting for Nonvolatile Memory

Cited by 8 publications

References 29 publications

NVMSorting: Efficient Sorting on Non-Volatile Memory

NVMSorting: Efficient Sorting on Non-Volatile Memory

Fast continuous streaming sort in big streaming data environment under fixed-size single storage

Correct and stable sorting for overflow streaming data with a limited storage size and a uniprocessor

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