11 citations · 19 across the 5 of their papers we have counts for
6 papers
GPU-accelerated single-cell analysis at scale with rapids-singlecell
Severin Dicks, Lukas Heumos, Lilly May +10
Single-cell sequencing technologies reveal cellular heterogeneity at high resolution, advancing our understanding of biological complexity. As datasets start to scale to tens of mi…
CAGRA: Highly Parallel Graph Construction and Approximate Nearest Neighbor Search for GPUs
Hiroyuki Ootomo, Akira Naruse, Corey Nolet +3
Approximate Nearest Neighbor Search (ANNS) plays a critical role in various disciplines spanning data mining and artificial intelligence, from information retrieval and computer vi…
cuSLINK: Single-linkage Agglomerative Clustering on the GPU
Corey J. Nolet, Divye Gala, Alex Fender +6
In this paper, we propose cuSLINK, a novel and state-of-the-art reformulation of the SLINK algorithm on the GPU which requires only space and uses a parameter to trade…
GPU Semiring Primitives for Sparse Neighborhood Methods
Corey J. Nolet, Divye Gala, Edward Raff +4
High-performance primitives for mathematical operations on sparse vectors must deal with the challenges of skewed degree distributions and limits on memory consumption that are typ…
Bringing UMAP Closer to the Speed of Light with GPU Acceleration
Corey J. Nolet, Victor Lafargue, Edward Raff +4
The Uniform Manifold Approximation and Projection (UMAP) algorithm has become widely popular for its ease of use, quality of results, and support for exploratory, unsupervised, sup…
Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence
Sebastian Raschka, Joshua Patterson, Corey Nolet
Smarter applications are making better use of the insights gleaned from data, having an impact on every industry and research discipline. At the core of this revolution lies the to…