24 citations · 43 across the 6 of their papers we have counts for
6 papers
A Study of Mixed Precision Strategies for GMRES on GPUs
Jennifer A. Loe, Christian A. Glusa, Ichitaro Yamazaki +2
Support for lower precision computation is becoming more common in accelerator hardware due to lower power usage, reduced data movement and increased computational performance. How…
Experimental Evaluation of Multiprecision Strategies for GMRES on GPUs
Jennifer A. Loe, Christian A. Glusa, Ichitaro Yamazaki +2
Support for lower precision computation is becoming more common in accelerator hardware due to lower power usage, reduced data movement and increased computational performance. How…
A Survey of Numerical Methods Utilizing Mixed Precision Arithmetic
Ahmad Abdelfattah, Hartwig Anzt, Erik G. Boman +22
Within the past years, hardware vendors have started designing low precision special function units in response to the demand of the Machine Learning community and their demand for…
Toward Efficient Polynomial Preconditioning for GMRES
Jennifer A. Loe, Ronald B. Morgan
We present a polynomial preconditioner for solving large systems of linear equations. The polynomial is derived from the minimum residual polynomial (the GMRES polynomial) and is m…
Polynomial Preconditioned GMRES to Reduce Communication in Parallel Computing
Jennifer A. Loe, Heidi K. Thornquist, Erik G. Boman
Polynomial preconditioning with the GMRES minimal residual polynomial has the potential to greatly reduce orthogonalization costs, making it useful for communication reduction. We…
Polynomial Preconditioned Arnoldi
Mark Embree, Jennifer A. Loe, Ronald B. Morgan
Polynomial preconditioning can improve the convergence of the Arnoldi method for computing eigenvalues. Such preconditioning significantly reduces the cost of orthogonalization; fo…