PDExa: Optimized software methods for solving partial differential equations on exascale supercomputers

Project overview

The project PDExa, funded by the German Federal Ministry of Education and Research (BMBF) through the initiative SCALEXA, project id 16ME0637K, develops novel software method for efficiently solving partial differential equations on supercomputer scale. The project combines the expertise of five partners at Technical University of Munich, the University of Augsburg, Karlsruhe Institute of Technology, and Ruhr University Bochum. We aim at developing modern high-order finite element algorithms that can utilize the computational power of modern CPU and GPU hardware, which can be applied to challenging application problems in fluid dynamics.



BMBF Corporate Design

Project partners

PI Research focus
Prof. Dr. Hartwig Anzt
Karlsruhe Institute of Technology

Linear algebra algorithms for GPUs

Schwarz-type preconditioners with batched local solvers

Prof. Dr. Katharina Kormann
Ruhr University Bochum

Mixed-Precision Solvers for Implicit Time Stepping

Structure-preserving Finite Element Methods

Prof. Dr. Martin Kronbichler
University of Augsburg

Node-level performance tuning of matrix-free algorithms

Multigrid solvers for CPU and GPU architectures

Prof. Dr. Martin Schulz

Technical University of Munich

Cross-platform abstraction

Large-scale scalability

Prof. Dr. Wolfgang A. Wall
Technical University of Munich
Development of CFD application solver ExaDG
Application to LES to gas emission and biomedicine

Open Positions

  • PostDoc or Ph.D. position at University of Augsburg: Development of matrix-free algorithms for CPUs and GPUs, performance tuning, see the official announcement
  • PostDoc or Ph.D. position at Ruhr University Bochum: Development of mixed-precision algorithms for time-dependent problems with structure-preserving finite elements, see the official announcement
  • PostDoc or Ph.D. position at Technical University of Munich (Department of Computer Engineering): Performance Evaluation and development of novel parallel programming abstractions for Exascale-class systems.

High Performance Computing