7 citations · 8 across the 5 of their papers we have counts for
5 papers
Distributed-Memory Parallel Algorithms for Fixed-Radius Near Neighbor Graph Construction
Gabriel Raulet, Dmitriy Morozov, Aydin Buluc +1
Computing fixed-radius near-neighbor graphs is an important first step for many data analysis algorithms. Near-neighbor graphs connect points that are close under some metric, endo…
Sparsity-Aware Communication for Distributed Graph Neural Network Training
Ujjaini Mukhodopadhyay, Alok Tripathy, Oguz Selvitopi +2
Graph Neural Networks (GNNs) are a computationally efficient method to learn embeddings and classifications on graph data. However, GNN training has low computational intensity, ma…
Scaling Graph Neural Networks for Particle Track Reconstruction
Alok Tripathy, Alina Lazar, Xiangyang Ju +3
Particle track reconstruction is an important problem in high-energy physics (HEP), necessary to study properties of subatomic particles. Traditional track reconstruction algorithm…
RDMA-Based Algorithms for Sparse Matrix Multiplication on GPUs
Benjamin Brock, Aydın Buluç, Katherine Yelick
Sparse matrix multiplication is an important kernel for large-scale graph processing and other data-intensive applications. In this paper, we implement various asynchronous, RDMA-b…
21st Century Computer Architecture
Mark D. Hill, Sarita Adve, Luis Ceze +7
Because most technology and computer architecture innovations were (intentionally) invisible to higher layers, application and other software developers could reap the benefits of…