This minisymposium took place at the SIAM Conference on Computational Science and Engineering, March 2, 2021. This page makes available slides from some of the talks.
Minisymposium description: Reduced precision floating-point arithmetic, such as IEEE half precision and bfloat16, is increasingly available in hardware. Low precision computations promise major increases in speed and reductions in data communication costs, but they also bring an increased risk of overflow, underflow, and loss of accuracy. One way to improve the results of low precision computations is to use stochastic rounding instead of round to nearest, and this is proving popular in machine learning. This minisymposium will discuss recent advances in exploitation and analysis of reduced precision arithmetic and stochastic rounding.
Algorithms for Stochastically Rounded Elementary Arithmetic Operations in IEEE 754 Floating-Point Arithmetic Massimiliano Fasi, Örebro University, Sweden; Mantas Mikaitis, University of Manchester, United Kingdom. Abstract. Slides.
Reduced Precision Elementary Functions Jean-Michel Muller, ENS Lyon, France. Abstract. Slides.
Effect of Reduced Precision and Stochastic Rounding in the Numerical Solution of Parabolic Equations Matteo Croci and Michael B. Giles, University of Oxford, United Kingdom. Abstract. Slides.
Stochastic Rounding and its Probabilistic Backward Error Analysis Michael P. Connolly and Nicholas J. Higham, University of Manchester, United Kingdom; Theo Mary, Sorbonne Universités and CNRS, France. Abstract. Slides.
Stochastic Rounding in Weather and Climate Models Milan Kloewer, Edmund Paxton, and Matthew Chantry, University of Oxford, United Kingdom Abstract. Slides.
Nick Higham 