https://github.com/tyler-hilbert/cuda-kmeans

Performance test for K-Means written from scratch in CUDA
https://github.com/tyler-hilbert/cuda-kmeans

cuda kmeans-clustering machine-learning nsight

Last synced: 21 days ago
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Performance test for K-Means written from scratch in CUDA

• Host: GitHub
• URL: https://github.com/tyler-hilbert/cuda-kmeans
• Owner: Tyler-Hilbert
• Created: 2024-08-07T21:28:30.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2024-10-02T15:34:35.000Z (3 months ago)
• Last Synced: 2024-12-10T22:41:24.474Z (21 days ago)
• Topics: cuda, kmeans-clustering, machine-learning, nsight
• Language: C++
• Homepage:
• Size: 327 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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README

        
# K-Means implemented from scratch using CUDA  

## Usage  

```C++

KMeans_CUDA model (data, N, D, K); // Where data is AoS, N is number of data points, D is number of dimensions and K is number of clusters

model.one_epoch(); // Trains one epoch

model.print_predictions(); // Prints the classifications. Can be commented out.

// printf ("Error: %f\n", model.compute_error()); // Uncomment to print error

```  

## Kernels

```C++

// Updates each centroid using d_sum and d_count

//    where the index is d * centroid number (out of k).

// d: number of dimensions

// k: number of clusters

__global__ void update_centroids(

    float *d_centroids,

    const float *d_sum,

    const int *d_count,

    int d,

    int k

);

```  

```C++

// Computes the sum (d_sum) and count (d_count) 

//    for each of the k clusters labeled in d_centroids.

// n: number of data points

// d: number of dimensions

// k: number of clusters

// Uses shared memory of 3*k*d

__global__ void sum_and_count(

    const float *d_data,

    const float *d_centroids,

    float *d_sum,

    int *d_count,

    int n,

    int d,

    int k

);

```  

```C++

// Computes error and updates d_error

// n: number of data points

// d: number of dimensions

// k: number of clusters

__global__ void calculate_error(

    const float *d_data,

    const float *d_centroids,

    float *d_error,

    int n,

    int d,

    int k

) {

```  

## Performance  

Go to: https://github.com/Tyler-Hilbert/CUDA-KMeans/tree/52db75728794449dc152989c648e03b632d24c08 for most recent performance tests.  

## Performance compared to scikit-learn and ArrayFire  

It is shown that this implementations of K-Means outperforms scitkit-learn and ArrayFire on a T4.  

![CUDA KMeans Performance vs scikit-learn and ArrayFire](https://raw.githubusercontent.com/Tyler-Hilbert/CUDA-KMeans/main/Performance/Comparison.png)  

## Usage Continued  

To compile the test program:  

$git clone https://github.com/Tyler-Hilbert/CUDA-KMeans.git  

$cd CUDA-KMeans  

$nvcc main.cpp KMeans_CUDA.cu -o kmeans  

$./kmeans