187 citations · 1k across the 59 of their papers we have counts for
64 papers
Fast Algorithms for Scheduling Many-body Correlation Functions on Accelerators
Oguz Selvitopi, Emin Ozturk, Jie Chen +3
Computation of correlation functions is a key operation in Lattice quantum chromodynamics (LQCD) simulations to extract nuclear physics observables. These functions involve many bi…
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…
Tackling Parallelization Challenges of Randomized Preconditioners With Dependency Tracking
Tianyu Liang, Chao Chen, Yotam Yaniv +6
Incomplete Cholesky (IC) preconditioners are the standard black-box, memory-efficient first resort for large graph Laplacian systems, but classical IC can break down on irregular g…
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…
Parallelizing the Approximate Minimum Degree Ordering Algorithm: Strategies and Evaluation
Yen-Hsiang Chang, Aydın Buluç, James Demmel
The approximate minimum degree algorithm is widely used before numerical factorization to reduce fill-in for sparse matrices. While considerable attention has been given to the num…