Impact & Valorization

Parallel-in-time for your HPC application?

Impact in applications


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.


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.

Want to know what time parallelization can offer for you? Interested to try it yourself?

Impact in science & industry

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.


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.

Want to know what TIME-X can offer for you? Interested to try it yourself?

Our software

Underneath, we list a selection of software developed by the TIME-X consortium, mainly relating to the targeted application domains.

Adaptive time parallization for molecular dynamics simulations

An adaptive implementation of the parareal algorithm for molecular dynamics (MD). The dynamics of the system is simulated using the MD software LAMMPS, which can model 2D or 3D systems with sizes ranging from only a few particles to billions. Simulations can be sped up by factor of 20.

Time parallelization of electric machines at Bosch

An in-house implementation of parareal to optimize and analyze Bosch’s motors, in particular for e-mobility applications. It is tightly integrated in Bosch’s electric machine simulation code to quickly compute the steady-state behavior of electric machines. Led to 28 times faster simulation approach.

Parallel-in-time for quenching problems at CERN

A high-performance-computing implementation of parareal that accelerates the simulation of quenching of superconducting accelerator magnets. Those simulations are necessary to ensure operational safety at CERN. Various what-if scenarios can now be simulated in acceptabel time.

DynMPI: dynamic management of resources with MPI

The DynMPI publicly available software realizes dynamic resources with MPI Sessions and PMIx. It provides extensions of the MPI Sessions Interface for handling changes of the application’s resources during runtime. The software is based on the publicly available Open MPI, OpenPMIx and PRRTE implementations.

pySDC: Python implementation of spectral deferred correction

The pySDC software is intended for rapid prototyping and educational purposes. New ideas such as sweepers or predictors can be tested and first toy problems can be easily implemented. It includes the integration of adaptivity and first resilience test cases and first steps toward GPU usage are taken.

SWEET (climate & weather)

This software allows a fast exploration & prototyping of time discretization methods for PDEs. The main applications are certain dynamical cores for climate & weather simulations as used by, e.g., the European Centre for Medium-Range Weather Forecasts.

TEMF Parareal for electromagnitcs

TEMF Parareal is an open-source implementation that allows for local parallel execution and for parallel execution over a cluster of machines, possibly combined with local parallel execution at each machine of the cluster. Speed-up of factor 10 realized.

Time parallelization for cardiac electrophysiology

Implementation of explicit stabilized methods in a parallel-in-time context for solving stiff differential equations appearing for instance in monodomain models in cardiac electrophysiology. These methods are well suited for integration with PFASST.

Short stories on our impact

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