OpsiClear

OpsiClear / flash-kmeans-cuda

Public

Cuda implemenation of flash-kmeans, 2x faster

18
1
100% credibility
Found May 04, 2026 at 18 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Cuda
AI Summary

An optimized library for performing fast batched K-Means clustering using Euclidean distance on NVIDIA GPUs.

How It Works

1
🔍 Discover fast clustering

You hear about a tool that groups huge sets of data points into clusters much faster than usual, perfect for your analysis needs.

2
💻 Add to your setup

You download it and integrate it simply into your data processing environment on a computer with a strong graphics card.

3
📊 Load your data

You prepare your collection of data points, like measurements or features, ready for grouping.

4
⚙️ Choose groups

You decide how many clusters or groups you want the data divided into.

5
⚡ Run the magic

You start the process and watch it zoom through massive data in seconds, feeling the speed boost right away!

6
📈 Review results

You receive the cluster labels for each point and the new center points for each group.

🎉 Analysis unlocked

Your data grouping is now incredibly quick, letting you explore insights and patterns faster than ever.

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AI-Generated Review

What is flash-kmeans-cuda?

This CUDA project delivers a PyTorch-compatible library for batched Euclidean K-Means clustering on NVIDIA GPUs, porting the flash-kmeans API with hand-written kernels that hit up to 2x faster speeds than the Triton original. It targets fp16 workloads on Ampere/Ada hardware like RTX 4090, handling large point sets (N up to 500k+) and clusters (K up to 8k) in dimensions like D=128. Developers get a simple drop-in: swap imports and run `batch_kmeans_Euclid(x, K)` for instant acceleration in embedding clustering pipelines.

Why is it gaining traction?

It crushes the assignment hot path—92-185 TFLOPS vs Triton's 38-126—yielding 1.5-4x end-to-end speedups on benchmark shapes, verified by quality checks within 0.03% inertia drift. As a CUDA GitHub example among projects like cuda github samples and toolkit tests, it offers editable kernels, GitHub Actions CI for wheels/shared libs, and env flags for tuning, making it a practical faster alternative to stock flash-kmeans or PyTorch baselines.

Who should use this?

GPU ML engineers clustering high-dim embeddings (e.g., 128D vectors from LLMs) at scale, or researchers prototyping kmeans in torch workflows needing sub-second iterations on 4090s. It's for teams evaluating CUDA GitHub projects for custom accel, like flash-kmeans forks or quantum sims via github cuda quantum.

Verdict

Grab it if you're on target hardware and need raw speed now—benchmarks deliver—but with 18 stars and 1.0% credibility, treat as alpha: build from source, run the included quality scripts first. Solid docs and tests make it a low-risk CUDA GitHub tutorial to hack on.

(198 words)

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