2 citations · 2 across the 2 of their papers we have counts for
3 papers
Communication-Avoiding Linear Algebraic Kernel K-Means on GPUs
Julian Bellavita, Matthew Rubino, Nakul Iyer +4
Clustering is an important tool in data analysis, with K-means being popular for its simplicity and versatility. However, it cannot handle non-linearly separable clusters. Kernel K…
Parallel GPU-Enabled Algorithms for SpGEMM on Arbitrary Semirings with Hybrid Communication
Thomas McFarland, Julian Bellavita, Giulia Guidi
Sparse General Matrix Multiply (SpGEMM) is key for various High-Performance Computing (HPC) applications such as genomics and graph analytics. Using the semiring abstraction, many…
Popcorn: Accelerating Kernel K-means on GPUs through Sparse Linear Algebra
Julian Bellavita, Thomas Pasquali, Laura Del Rio Martin +2
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-linea…