mp-kmeans

A mixed-precision algorithm of k-means is designed towards understanding of the low precision arithmetic for Euclidean distance computations. By performing simulations across data with various settings, we showcase that decreased precision for k-means computing only results in a minor increase in sum of squared errors while not necessarily leading to degrading performance regarding clustering results.

mp-kmeans is a CUDA-accelerated mixed-precision implementation of k-means designed for large-scale clustering workloads. It provides multiple precision paths (FP16/BF16/FP32/FP64 and mixed fallback modes) to balance throughput and numerical stability.

Features

• Mixed-precision Euclidean distance kernels for GPU k-means.
• Uniform precision modes (fp16, bf16, fp32, fp64) and mixed modes (e.g. fp16_fp32, fp16_fp64).
• CUDA center update kernels with automatic empty-cluster reinitialization.
• Configurable normalization (standard, l2, minmax) for robust behavior on unnormalized datasets.

Installation

pip install mp-kmeans

This package targets CUDA-enabled environments and depends on PyTorch with CUDA support.

Quick Start

import torch
from mp_kmeans import KMeansPlusPlus, make_blobs_gpu

X, _ = make_blobs_gpu(
    n_samples=100_000,
    n_features=128,
    n_centers=100,
    cluster_std=1.0,
    random_state=42,
)

model = KMeansPlusPlus(
    n_clusters=100,
    kernel="fp16_fp32",
    kappa=10.0,
    max_iter=300,
    tol=1e-8,
    normalize="standard",
    random_state=42,
)

model.fit(X)
print(model.n_iter_, model.inertia_)

Kernel Modes

• Uniform: fp16_uniform, bf16_uniform, tf32_uniform, fp32_uniform, fp64_uniform
• Mixed: fp16_fp32, bf16_fp32, fp32_fp32, fp16_fp64, bf16_fp64, tf32_fp64, fp32_fp64
• Advanced: fp64_fp16, fp64_bf16, fp64_tf32, fp64_fp32_gemm

Citation

@techreport{ccl24,
  author = "Erin Carson and Xinye Chen and Xiaobo Liu",
  title = "Computing $k$-means in Mixed Precision",
  month = jul,
  year = 2026,
  type = "{ArXiv}:2407.12208 [math.{NA}]",
  url = "https://arxiv.org/abs/2407.12208"
}