Awesome Similarity Search
πŸ“„Papers🧭TopicsπŸ‘₯AuthorsπŸ”₯TrendingπŸ—ΊοΈMapπŸ†LeaderboardsπŸ“šPacksπŸ› οΈToolsπŸ“BlogsπŸ€–Ask AIβœ‰οΈNewsletterπŸš€Pro
+ Add Paper

← all papers Β· overview

An $O(N)$ Sorting Algorithm: Machine Learning Sort

Hanqing ZhaoΒ·Yuehan LuoΒ·2018
Citations0GitHub0β˜…HF0
𝕏inβœ‰οΈ
arXiv:1805.04272 β†—Google Scholar β†—Semantic Scholar β†—
cs.LGcs.DSstat.ML

Abstract

We propose an $O(N\cdot M)$ sorting algorithm by Machine Learning method, which shows a huge potential sorting big data. This sorting algorithm can be applied to parallel sorting and is suitable for GPU or TPU acceleration. Furthermore, we discuss the application of this algorithm to sparse hash table.

Related papers

  • Learning from Data to Speed-up Sorted Table Search Procedures: Methodology and Practical Guidelines (2020)β€”
  • Stochastic Optimization of Sorting Networks via Continuous Relaxations (2019)β€”
  • Recombinant Sort: N-Dimensional Cartesian Spaced Algorithm Designed from Synergetic Combination of Hashing, Bucket, Counting and Radix Sort (2021)β€”
  • SIMD-Optimized Search Over Sorted Data (2021)β€”
  • Robust and Efficient Sorting with Offset-Value Coding (2022)β€”
  • Scalable Discrete Supervised Hash Learning with Asymmetric Matrix Factorization (2016)β€”
  • A Memory Bandwidth-Efficient Hybrid Radix Sort on GPUs (2017)β€”
  • Accelerating Divisible Load Processing Through Machine Learning: A Practical Framework for Large-Scale Workloads (2026)β€”

Stay Updated

E-Mail Digest

Submit a paper Β· Privacy Β· Terms

Β© 2026 Awesome Papers.