| A survey of numerical linear algebra methods utilizing mixed-precision arithmetic A Abdelfattah, H Anzt, EG Boman, E Carson, T Cojean, J Dongarra, A Fox, ... The International Journal of High Performance Computing Applications 35 (4 …, 2021 | 202 | 2021 |
| Squeezing a matrix into half precision, with an application to solving linear systems NJ Higham, S Pranesh, M Zounon SIAM journal on scientific computing 41 (4), A2536-A2551, 2019 | 91 | 2019 |
| Simulating Low Precision Floating-Point Arithmetic NJ Higham, S Pranesh SIAM Journal on Scientific Computing 41 (5), C585–C602, 2019 | 87 | 2019 |
| Mixed precision block fused multiply-add: Error analysis and application to GPU tensor cores P Blanchard, NJ Higham, F Lopez, T Mary, S Pranesh SIAM Journal on Scientific Computing 42 (3), C124-C141, 2020 | 70 | 2020 |
| The design of fast and energy-efficient linear solvers: On the potential of half-precision arithmetic and iterative refinement techniques A Haidar, A Abdelfattah, M Zounon, P Wu, S Pranesh, S Tomov, ... International conference on computational science, 586-600, 2018 | 55 | 2018 |
| Exploiting lower precision arithmetic in solving symmetric positive definite linear systems and least squares problems NJ Higham, S Pranesh SIAM Journal on Scientific Computing 43 (1), A258-A277, 2021 | 49* | 2021 |
| Faster computation of the Karhunen–Loève expansion using its domain independence property S Pranesh, D Ghosh Computer Methods in Applied Mechanics and Engineering 285, 125-145, 2015 | 46 | 2015 |
| Numerical behavior of NVIDIA tensor cores M Fasi, NJ Higham, M Mikaitis, S Pranesh PeerJ Computer Science 7, e330, 2021 | 44 | 2021 |
| Three-precision GMRES-based iterative refinement for least squares problems E Carson, NJ Higham, S Pranesh SIAM Journal on Scientific Computing 42 (6), A4063-A4083, 2020 | 43* | 2020 |
| Addressing the curse of dimensionality in SSFEM using the dependence of eigenvalues in KL expansion on domain size S Pranesh, D Ghosh Computer Methods in Applied Mechanics and Engineering 311, 457-475, 2016 | 22 | 2016 |
| Random matrices generating large growth in LU factorization with pivoting DJ Higham, NJ Higham, S Pranesh SIAM Journal on Matrix Analysis and Applications 42 (1), 185-201, 2021 | 19 | 2021 |
| Cost reduction of stochastic Galerkin method by adaptive identification of significant polynomial chaos bases for elliptic equations S Pranesh, D Ghosh Computer Methods in Applied Mechanics and Engineering 340, 54-69, 2018 | 11 | 2018 |
| A FETI-DP based parallel hybrid stochastic finite element method for large stochastic systems S Pranesh, D Ghosh Computers & Structures 195, 64-73, 2018 | 9 | 2018 |
| D2. 6 Prototype Software for Eigenvalue Problem Solvers M Myllykoski, L Karlsson, B Kågström, M Eljammaly, S Pranesh, ... NLAFET Consortium; Umeå University, 2018 | 4 | 2018 |
| Randomized low rank matrix approximation: Rounding error analysis and a mixed precision algorithm MP Connolly, NJ Higham, S Pranesh | 3 | 2022 |
| Low precision floating-point formats: The wild west of computer arithmetic S Pranesh SIAM News, November, 2019 | 3 | 2019 |
| Development of an efficient domain decomposition algorithm for solving large stochastic mechanics problems S Pranesh | 1 | 2021 |
| Backward error and condition number of a generalized Sylvester equation, with application to the stochastic Galerkin method S Pranesh Linear Algebra and its Applications 594, 95-116, 2020 | 1 | 2020 |
| D7. 8 Release of the NLAFET library B Kågström, M Myllykoski, L Karlsson, CC Kjelgaard Mikkelsen, S Cayrols, ... NLAFET Consortium; Umeå University, 2019 | | 2019 |
Nicholas J. HighamRoyal Society Research Prof, Richardson Prof of Applied Mathematics, University of ManchesterVerified email at manchester.ac.uk
