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Fault Oblivious Eigenvalue Solver
Jayanta Mukherjee, Xuejiao Kang, David F. Gleich +2
Eigenvalue problems serve as fundamental substrates for applications in large-scale scientific simulations and machine learning, often requiring computation on massively parallel p…
Dominant H-Eigenvectors of Tensor Kronecker Products Do Not Decouple
Ayush Kulkarni, Charles Colley, David F. Gleich
We illustrate a counterexample to an open question related to the dominant H-eigenvector of a Kronecker product of tensors. For matrices and Z-eigenvectors of tensors, the dominant…
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…
Powers of Magnetic Graph Matrix: Fourier Spectrum, Walk Compression, and Applications
Yinan Huang, David F. Gleich, Pan Li
Magnetic graphs, originally developed to model quantum systems under magnetic fields, have recently emerged as a powerful framework for analyzing complex directed networks. Existin…
An Empirical Study of Conjugate Gradient Preconditioners for Solving Symmetric Positive Definite Systems of Linear Equations
Marc A. Tunnell, David F. Gleich
Despite hundreds of papers on preconditioned linear systems of equations, there remains a significant lack of comprehensive performance benchmarks comparing various preconditioners…