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.
Authors
- Huifeng Wu (ORCID: https://orcid.org/0000-0002-7189-1205)
- Juntao Jiang (ORCID: https://orcid.org/0000-0003-2139-6449)
- Muxuan Gao (ORCID: https://orcid.org/0000-0002-5873-0271)
- Yong Liu (ORCID: https://orcid.org/0000-0003-4822-8939)
- Jun Chen (ORCID: https://orcid.org/0000-0001-6568-8801)
- Lei Sun (ORCID: https://orcid.org/0000-0003-1451-1875)
Institutions
- Hangzhou Normal University (CN)
- Hangzhou Dianzi University (CN)
- Zhejiang University (CN)
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
Funders
- National Natural Science Foundation of China