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Martin Schreiber

This website provides some information about myself, my finished, not finished and running projects.

If you need an unpublished code, don't hesistate to drop me an E-Mail!

Research interests / expertise:

  • Architectures: CPU, GPU, (XeonPhi,) FPGAs
  • Parallel programming standards: OpenMP, OpenCL, MPI, PGAS (X10)
  • Applications (among others): Weather, Climate, Ocean
  • Algorithms: development of new algorithms for scientific computing
  • Parallelization: new algorithms on HPC architectures, new parallelization concepts/models, parallelization in time
  • Mesh: Focus on dynamically adaptive grids based on space-filling curves
  • Compute resources: Dynamic resource scheduling
  • Realtime: Interactive simulations
  • Visualization: Efficient on- and offline processing


  • Co-Leader of special interest group on "Next Generation Time-Stepping methods", UK ( )
  • HPC NEMO group, Europe
  • Society for Industrial and Applied Mathematics (SIAM)
  • Early-Career Research Network (ECRN), Exeter, UK
  • Advanced Research Computing (ARC) group, Exeter, UK
I was appointed as a proleptic lecturer in 2015 at the University of Exeter. My current focus is in various HPC areas such as Biological parameter estimation on accelerator cards, parallelization in time methods, ocean simulations, etc.

In 2010, I wrote my diploma thesis in Computer Science at the Technische Universitaet Muenchen (TUM) on simulation and visualization of the free surface lattice Boltzmann equation on GPUs.

At the end of 2010, I joined the research group of Prof. Bungartz at TUM as a doctoral candidate where I worked in the Invasive Computing Transregio Project (DFG funded). My work in this project was two-folded: In collaboration with other members of the project I redesigned algorithms to support dynamical resource management on embedded systems. For high-performance systems, I developed a new run-length encoded cluster-based parallelization method for efficiently running simulations on dynamically adaptive triangular grids with MPI+X parallelization models and presented the benefits of dynamic resource management for Tsunami parameter studies.
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