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Fine-Grained Parallel Graph Algorithms Can Be Both Fast and Scalable
Yuqing Wang, Charles Colley, Brian Wheatman +6
Making graph algorithms fast and scalable on traditional architectures and GPUs often involves breaking them into large, coarse-grained computational blocks that access similar reg…
A Parallel Projection Method for Metric Constrained Optimization
Cameron Ruggles, Nate Veldt, David F. Gleich
Many clustering applications in machine learning and data mining rely on solving metric-constrained optimization problems. These problems are characterized by constraints…
A Parallel Min-Cut Algorithm using Iteratively Reweighted Least Squares
Yao Zhu, David F. Gleich
We present a parallel algorithm for the undirected -mincut problem with floating-point valued weights. Our overarching algorithm uses an iteratively reweighted least squares f…
Direct QR factorizations for tall-and-skinny matrices in MapReduce architectures
Austin R. Benson, David F. Gleich, James Demmel
The QR factorization and the SVD are two fundamental matrix decompositions with applications throughout scientific computing and data analysis. For matrices with many more rows tha…