OnSort: An <i>O</i>(<i>n</i>) Comparison-Free Sorter for Large-Scale Dataset With Parallel Prefetching and Sparse-Aware Mechanism

This paper proposes OnSort, a parallel comparison-free sorting architecture with O(n) time complexity, utilizing the SRAM structure to support large-scale datasets efficiently. The performance of existing comparison-free sorters is limited by uneven value distribution and variable element numbers. To address these issues, we introduce a parallel prefetching strategy to accelerate the indexing process and a sparse-aware mechanism to narrow the indexing search range. Furthermore, OnSort implements streaming execution through a pipelined design, thereby optimizing the previously overlooked latency of the counting phase. Experimental results show that, under the configuration of sorting 65,536 16-bit data elements, OnSort achieves a 1.97× speedup and a 22.6× throughput-to-area ratio compared to the existing design. The source code is available at https://github.com/gmx-hub/OnSort.

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Publication Details

Journal
IEEE Transactions on Circuits & Systems II Express Briefs
Published
2025-05-16
DOI
https://doi.org/10.1109/tcsii.2025.3570797
Primary Topic
Parallel Computing and Optimization Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

OnSort: An O(n) Comparison-Free Sorter for Large-Scale Dataset With Parallel Prefetching and Sparse-Aware Mechanism

Huifeng Wu, Juntao Jiang, Muxuan Gao, Yong Liu et al.
IEEE Transactions on Circuits & Systems II Express Briefs
Parallel Computing and Optimization Techniques
article

OnSort: An O(n) Comparison-Free Sorter for Large-Scale Dataset With Parallel Prefetching and Sparse-Aware Mechanism

Huifeng Wu, Juntao Jiang, Muxuan Gao, Yong Liu, Jun Chen, Lei Sun
article en

Abstract

This paper proposes OnSort, a parallel comparison-free sorting architecture with O(n) time complexity, utilizing the SRAM structure to support large-scale datasets efficiently. The performance of existing comparison-free sorters is limited by uneven value distribution and variable element numbers. To address these issues, we introduce a parallel prefetching strategy to accelerate the indexing process and a sparse-aware mechanism to narrow the indexing search range. Furthermore, OnSort implements streaming execution through a pipelined design, thereby optimizing the previously overlooked latency of the counting phase. Experimental results show that, under the configuration of sorting 65,536 16-bit data elements, OnSort achieves a 1.97× speedup and a 22.6× throughput-to-area ratio compared to the existing design. The source code is available at https://github.com/gmx-hub/OnSort.

IEEE Transactions on Circuits & Systems II Express BriefsVol. 72(7)
Hangzhou Normal University (CN), Hangzhou Dianzi University (CN), Zhejiang University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 91%
Parallel Computing and Optimization Techniques
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