Popcorn: Accelerating Kernel K-means on GPUs through Sparse Linear Algebra

TL;DR

Popcorn is a GPU-accelerated, open-source implementation of Kernel K-means that uses sparse linear algebra to significantly speed up clustering of large datasets, enabling practical non-linear clustering.

Contribution

This paper introduces a novel formulation of Kernel K-means using sparse linear algebra, enabling efficient GPU implementation with substantial speedups.

Findings

Achieves up to 123.8x speedup over CPU implementation

Outperforms non-sparse GPU Kernel K-means by up to 2.6x

First open-source GPU Kernel K-means implementation

Abstract

K-means is a popular clustering algorithm with significant applications in numerous scientific and engineering areas. One drawback of K-means is its inability to identify non-linearly separable clusters, which may lead to inaccurate solutions in certain cases. Kernel K-means is a variant of classical K-means that can find non-linearly separable clusters. However, it scales quadratically with respect to the size of the dataset, taking several minutes to cluster even medium-sized datasets on traditional CPU-based machines. In this paper, we present a formulation of Kernel K-means using sparse-dense matrix multiplication (SpMM) and sparse matrix-vector multiplication (SpMV), and we show that our formulation enables the rapid implementation of a fast GPU-based version of Kernel K-means with little programming effort. Our implementation, named Popcorn, is the first open-source GPU-based implementation of Kernel K-means. Popcorn achieves a speedup of up to 123.8x over a CPU implementation of Kernel K-means and a speedup of up to 2.6x over a GPU implementation of Kernel K-means that does not use sparse matrix computations. Our results support the effectiveness of sparse matrices as tools for efficient parallel programming.