# FT K-means: A High-Performance K-means on GPU with Fault Tolerance

arXiv: https://arxiv.org/abs/2408.01391 · HTML: https://mlsanity.com/paper/2408.01391/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 75 references that are in the map.
- First posted on arXiv 2 Aug 2024.

## Builds on (most cited first, 11 of 11)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence](https://mlsanity.com/paper/2002.04803/) (2020)
- [ByteTransformer: A High-Performance Transformer Boosted for Variable-Length Inputs](https://mlsanity.com/paper/2210.03052/) (2022)
- [Silent Data Corruptions at Scale](https://mlsanity.com/paper/2102.11245/) (2021)
- [Arithmetic-Intensity-Guided Fault Tolerance for Neural Network Inference on GPUs](https://mlsanity.com/paper/2104.09455/) (2021)
- [High-performance Effective Scientific Error-bounded Lossy Compression with Auto-tuned Multi-component Interpolation](https://mlsanity.com/paper/2311.12133/) (2023)
- [Anatomy of High-Performance GEMM with Online Fault Tolerance on GPUs](https://mlsanity.com/paper/2305.01024/) (2023)
- [Improving Performance of Iterative Methods by Lossy Checkponting](https://mlsanity.com/paper/1804.11268/) (2018)
- [FT-BLAS: A High Performance BLAS Implementation With Online Fault Tolerance](https://mlsanity.com/paper/2104.00897/) (2021)
- [TurboFFT: A High-Performance Fast Fourier Transform with Fault Tolerance on GPU](https://mlsanity.com/paper/2405.02520/) (2024)
- [FT-GEMM: A Fault Tolerant High Performance GEMM Implementation on x86 CPUs](https://mlsanity.com/paper/2305.02444/) (2023)

## Built on by (most cited first, 2 of 2)

- [FT-Transformer: Resilient and Reliable Transformer with End-to-End Fault Tolerant Attention](https://mlsanity.com/paper/2504.02211/) (2025)
- [Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels](https://mlsanity.com/paper/2508.21424/) (2025)

## Abstract (arXiv)

K-means is a widely used algorithm in clustering, however, its efficiency is primarily constrained by the computational cost of distance computing. Existing implementations suffer from suboptimal utilization of computational units and lack resilience against soft errors. To address these challenges, we introduce FT K-means, a high-performance GPU-accelerated implementation of K-means with online fault tolerance. We first present a stepwise optimization strategy that achieves competitive performance compared to NVIDIA's cuML library. We further improve FT K-means with a template-based code generation framework that supports different data types and adapts to different input shapes. A novel warp-level tensor-core error correction scheme is proposed to address the failure of existing fault tolerance methods due to memory asynchronization during copy operations. Our experimental evaluations on NVIDIA T4 GPU and A100 GPU demonstrate that FT K-means without fault tolerance outperforms cuML's K-means implementation, showing a performance increase of 10%-300% in scenarios involving irregular data shapes. Moreover, the fault tolerance feature of FT K-means introduces only an overhead of 11%, maintaining robust performance even with tens of errors injected per second.