5 papers
Neural Acceleration for Graph Partitioning
Joshua Dennis Booth, Vishvam Patel
Graph Partitioning is a critical problem in numerous scientific and engineering domains including social network analysis, VLSI design, and many more. Spectral methods are known to…
Neural Acceleration of Incomplete Cholesky Preconditioners
Joshua Dennis Booth, Hongyang Sun, Trevor Garnett
The solution of a sparse system of linear equations is ubiquitous in scientific applications. Iterative methods, such as the Preconditioned Conjugate Gradient method (PCG), are nor…
Heterogeneous Sparse Matrix-Vector Multiplication via Compressed Sparse Row Format
Phillip Allen Lane, Joshua Dennis Booth
Sparse matrix-vector multiplication (SpMV) is one of the most important kernels in high-performance computing (HPC), yet SpMV normally suffers from ill performance on many devices.…
Javelin: A Scalable Implementation for Sparse Incomplete LU Factorization
Joshua Dennis Booth, Gregory Bolet
In this work, we present a new scalable incomplete LU factorization framework called Javelin to be used as a preconditioner for solving sparse linear systems with iterative methods…
Basker: A Threaded Sparse LU Factorization Utilizing Hierarchical Parallelism and Data Layouts
Joshua Dennis Booth, Sivasankaran Rajamanickam, Heidi K. Thornquist
Scalable sparse LU factorization is critical for efficient numerical simulation of circuits and electrical power grids. In this work, we present a new scalable sparse direct solver…