Modern high-performance computing increasingly relies on hardware accelerators originally designed for artificial intelligence. These accelerators achieve exceptional performance by using low precision arithmetic, which is sufficient for machine learning tasks but much too inaccurate for most scientific applications. To harness these accelerators for scientific computing, one must develop new algorithms that combine low and high precision computations in a way that preserves accuracy while delivering significant gains in terms of speed and energy efficiency.

This PhD project will focus on developing mixed precision algorithms for large scale, sparse eigenvalue problems and matrix functions. These computational problems are central to many scientific and engineering applications, including quantum mechanics, materials science, and weather and climate modelling.

Modern high-performance computing increasingly relies on hardware accelerators originally designed for artificial intelligence. These accelerators achieve exceptional performance by using low precision arithmetic, which is sufficient for machine learning tasks but much too inaccurate for most scientific applications. To harness these accelerators for scientific computing, one must develop new algorithms that combine low and high precision computations in a way that preserves accuracy while delivering significant gains in terms of speed and energy efficiency.

This PhD project will focus on developing mixed precision algorithms for large scale, sparse eigenvalue problems and matrix functions. These computational problems are central to many scientific and engineering applications, including quantum mechanics, materials science, and weather and climate modelling.