Dr. Philipp Neumann, Universität Hamburg

Prof. Dr. Hans-Joachim Bungartz, Technische Universität München

Funded Partners

TaLPas (Task-basierte Lastverteilung und Auto-Tuning in der Partikelsimulation) is a three-year project (01/2017 - 12/2019). It is funded by the Federal Ministry of Education and Research (BMBF; call “Grundlagenorientierte Forschung für HPC-Software im Hoch- und Höchstleistungsrechnen”, grant number 01IH16008). The consortium of TaLPas consists of seven partners:

Prof. Dr. Thomas Ludwig, Dr. Philipp Neumann, Universität Hamburg

Prof Dr. Hans-Joachim Bungartz, Nikola Tchipev, Steffen Seckler, Technische Universität München

Dr. Colin W. Glass, Nils Urmersbach, HLRS/Universität Stuttgart

Dr. Guido Reina, Oliver Fernandes, VISUS/Universität Stuttgart

Prof. Dr. Felix Wolf, Dr. Sergei Shudler, Technische Universität Darmstadt

Jun.-Prof. Dr. Martin Horsch, Technische Universität Kaiserslautern

Prof. Dr. Jadran Vrabec, Matthias Heinen, Universität Paderborn


With the approaching exascale era, HPC architectures undergo radical changes. The increase in compute cores per processor/node and the rise of requires “MPI+X” programming approaches. Fault-tolerant approaches need to account for hard- and software failures at exascale. Optimal exploitation of respective supercomputers becomes more and more important, due to the high energy consumption of these huge systems. These and many other issues pose challenges for the programmers and the users of respective soft- and hardware. In particular, the usual tuning approach

  1. Choose an optimal algorithm; go to 2
  2. Optimize at node-level; investigate performance and pot. revise 1,2; go to 3
  3. Optimize at distributed memory level; investigate performance and pot. revise 1,2,3; go to 4
  4. Optimal software solution found

becomes very complex.

In this regard, the project TaLPas: Task-basierte Lastverteilung und Auto-Tuning in der Partikelsimulation targets an auto-tuning and task-based approach to high-performance particle simulations. Particle simulations are used in a wide range of problem settings such as molecular dynamics, fluid dynamics, or astrophysics.

The main goal of TaLPas is to provide a solution to fast and robust simulation of many, potentially dependent particle systems in a distributed environment.

This is required in many applications, including, but not limited to,

  • sampling in molecular dynamics: so-called “rare events”, e.g. droplet formation, require a multitude of molecular dynamics simulations to investigate the actual conditions of phase transition,
  • uncertainty quantification: various simulations are performed using different parametrisations to investigate the sensitivity of the parameters on the actual solution,
  • parameter identification: given, e.g., a set of experimental data and a molecular model, an optimal set of model parameters needs to be found to fit the model to the experiment.

For this purpose, TaLPas targets

  1. the development of innovative auto-tuning based particle simulation software in form of an open-source library to leverage optimal node-level performance. This will guarantee an optimal time-to-solution for small- to mid-sized particle simulations,
  2. the development of a scalable task scheduler to yield an optimal distribution of potentially dependent simulation tasks on available HPC compute resources,
  3. the combination of both auto-tuning based particle simulation and scalable task scheduler, augmented by an approach to resilience. This will guarantee robust, that is fault-tolerant, sampling evaluations on peta- and future exascale platforms.


Dr. Philipp Neumann, philipp.neumann@uni-hamburg.de



N. Tchipev et al. Towards Autotuning Between OpenMP Schemes for Molecular Dynamics on Intel Xeon Phi. SIAM CSE, Atlanta, 2017

P. Neumann et al. TaLPas: Task-basierte Lastverteilung und Auto-Tuning in der Partikelsimulation. Project teaser, Gauß-Allianz HPC-Status-Konferenz, Hamburg, 2016

Further Material