Stability Analysis and Performance Evaluation of Additive Mixed-Precision Runge-Kutta Methods
Ben Burnett, Sigal Gottlieb, Zachary J. Grant
Stability Analysis and Performance Evaluation of Additive Mixed-Precision Runge-Kutta Methods
Additive Runge-Kutta methods designed for preserving highly accurate solutions in mixed-precision computation were previously proposed and analyzed. These specially designed methods use reduced precision for the implicit computations and full precision for the explicit computations. In this work, we analyze the stability properties of these methods and their sensitivity to the low-precision rounding errors, and demonstrate their performance in terms of accuracy and efficiency. We develop codes in FORTRAN and Julia to solve nonlinear systems of ODEs and PDEs using the mixed-precision additive Runge-Kutta (MP-ARK) methods. The convergence, accuracy, and runtime of these methods are explored. We show that for a given level of accuracy, suitably chosen MP-ARK methods may provide significant reductions in runtime.
Mixed precision / Runge-Kutta methods / Additive methods / Accuracy
[1.] |
|
[2.] |
Abdelfattah, A., Tomov, S., Dongarra, J.J.: Fast batched matrix multiplication for small sizes using half-precision arithmetic on GPUs. In: 2019 IEEE International Parallel and Distributed Processing Symposium, IPDPS 2019, Rio de Janeiro, Brazil, May 20–24, pp. 111–122. IEEE (2019)
|
[3.] |
|
[4.] |
Burnett, B., Gottlieb, S., Grant, Z.J., Heryudono, A.: Evaluation, performance, of mixed-precision Runge-Kutta methods. In: IEEE High Performance Extreme Computing Conference (HPEC). Waltham, MA, USA 2021, 1–6 (2021). https://doi.org/10.1109/HPEC49654.2021.9622803
|
[5.] |
|
[6.] |
Butcher, J.C.: B-series: algebraic analysis of numerical methods. In: Springer Series in Computational Mathematics, 55. Springer, Cham, Switzerland (2021)
|
[7.] |
|
[8.] |
|
[9.] |
|
[10.] |
|
[11.] |
Gupta, S., Agrawal, A., Gopalakrishnan, K., Narayanan, P.: Deep learning with limited numerical precision. In: Proceedings of the 32nd International Conference on International Conference on Machine Learning, vol. 37, ICML’15, pp. 1737–1746. JMLR.org (2015)
|
[12.] |
Haidar, A., Tomov, S., Dongarra, J., Higham, N.J.: Harnessing GPU tensor cores for fast FP16 arithmetic to speed up mixed-precision iterative refinement solvers. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis, SC’18, pp. 47:1–47:11, Piscataway, NJ, USA. IEEE Press (2018)
|
[13.] |
Higham, N.J.: Error analysis for standard and GMRES-based iterative refinement in two and three-precisions. MIMS EPrint 2019.19, Manchester Institute for Mathematical Sciences, The University of Manchester, November (2019)
|
[14.] |
|
[15.] |
|
[16.] |
|
[17.] |
|
[18.] |
|
[19.] |
|
[20.] |
|
[21.] |
|
[22.] |
|
[23.] |
|
/
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