Impact & Valorization

Medicine

Challenge. Time is a critical factor in medical practice: during a surgery, there is no time to wait for a simulation result. Even today, with current supercomputers, the cost of serial time stepping is a major obstacle, making the time-to-solution too long. As a result, the value of current functional models in medicine remains unexploited.

PinT value. Parallel-in-time methods help to reduce the high time-to-solution typical for many medical problems and therefore bring these techniques closer to clinical practice. PinT methods will be at the core of the necessary HPC tools to resolve this bottleneck in a wide range of medical applications.

Electromagnetics

Challenge. Europe’s transition towards renewable energy has increased the importance of efficient and robust energy converters, in particular electrical machines for e-mobility. The simulation of such systems, however, is very time-consuming.

PinT value. Faster time-to-solution of the (multiphysical) electromagnetic simulations allows simulating models of higher accuracy, leading to higher energy efficiency of machines while maintaining or even increasing their performance. An even more significant impact on society is expected, for instance, power generation (fusion reactors) or medicine (improvements in heavy ion cancer therapy).

Drug design

Challenge. A central question in drug design is the ability to predict and optimize the affinity of a ligand (the drug) with a biological target (a binding pocket in a protein). For this, full molecular dynamics simulations are needed which remain computationally expensive due to the time-scale problem: the timestep of a full-atom molecular dynamics is in femtoseconds, while timescales of interest range from microseconds to hours.

PinT value. Time parallelization algorithms play a crucial role to bridge these timescales. Next to that, the techniques developed in TIME-X also improve both the accuracy and predictability of current algorithms.

Weather & climate

Challenge. The improvement of weather forecasts has mainly been realized by increasing the spatial resolution. This was possible thanks to a constantly increasing processor speed. However, this increase has slowed or even decreased over the last decade. Performance increases by parallel architectures (e.g., multi-core CPUs) are still not enough to improve the wall clock time of weather forecasts for current global models on kilometer scale.

PinT value. Parallel-in-time algorithms are a way to overcome such limitations, leading to a continuous improvement of these forecasts and ultimately contributing to our daily lives and saving even more human lives.

Scientific community

Multi-scale simulation. Multilevel, iterative solution strategies (typical of parallel-in-time methods) naturally match with the idea of most current multi-scale approaches. TIME-X explores such links explicitly.

Optimization and control. The scalability potential of parallel-in-time methods can be exploited even further in control and optimization problems, where forward-in-time simulation can act as a constraint. One then needs to design algorithms that integrate the parallel-in-time iterations with the iterations of the optimization algorithm. TIME-X has a transformative impact on computational practice in such contexts.

Uncertainty quantification. When the model contains some randomness, one needs to average over large numbers of simulations. TIME-X is readily combining parallel-in-time methods with methods for uncertainty quantification.

HPC software engineering. In TIME-X, we study software engineering techniques that can have great impact on how prototyping and testing of new algorithmic ideas can be efficiently done for a variety of hardware systems.

Industry

With Exascale computing, we have the potential to dramatically improve our capabilities in simulation and prediction, virtual experiment, rapid prototyping and real-time control. This will create a huge amount of new possibilities for industry and industry sectors. With the methods and software developed in TIME-X, we are able to exploit exascale power for:

• time-critical application, such as the examples from the Health and Medicine domains (see above),
• large-scale applications, such as the fusion or climate cases (see above).

The broad range of possible applications will enable new simulation-based services for, e.g., medical or engineering applications. These services will hide the complexity of exascale computing from the end-user, but they will deliver immediate response for time-critical applications.