About:

Efficient computation of solutions to partial differential equations on modern supercomputers is crucial for many scientific applications such as weather and climate prediction, fusion modelling, molecular dynamics and engineering applications. As we have reached the limit of Dennard scaling, which previously guaranteed faster processors due to smaller components, modern supercomputers now have orders of magnitude more processors than previous generations. Exascale supercomputers are capable of 10^18 floating point operations per second but making full use of this hardware for the solution of partial differential equations requires a deep understanding of the mathematical properties of the governing equations and how to discretise them in a way that enables calculations to be performed concurrently, that is in parallel.

Parallel-in-time algorithms, the topic of this workshop, enable parallel computation in the time domain, in addition to more traditional parallelism through spatial domain decomposition. Time-parallelism is significantly more challenging than spatial parallelism due to causality, since the state of the system at future times must depend on that at past times. However, the advent of the exascale era has inspired recent research in to these algorithms, leading to an explosive increase in the number of different algorithms available. The efficiency of these algorithms depends on tailoring the algorithm to the equations being solved and this requires interdisciplinary collaboration both with the application experts and with mathematicians working in adjacent areas.

This workshop will bring together such an interdisciplinary group of people to advance our understanding of these algorithms and open up new areas of collaboration and research.