Jens Henrik Göbbert

Overlapping of Communication and Computation in nb3dfft for 3D Fast Fourier Transformations

For efficiency and accuracy of Direct Numerical Simulations (DNS) of turbulent flows pseudo-spectral methods can be employed, where the governing equations are solved partly in Fourier space. The inhouse-developed 3d-FFT library nb3dfft is optimized to the special needs of pseudo-spectral DNS, particularly for the scientific code psOpen, used by the Institute for Combustion Technology at RWTH Aachen University. In this paper we discuss the method of overlapping communication and computation of multiple FFTs at the same time.

Extreme-Scale In Situ Visualization of Turbulent Flows on IBM Blue Gene/Q JUQUEEN

Extracting and analyzing detailed information from large simulations is of crucial importance for science. However, with the increasing problem size of current simulations, the process of visualizing and understanding big simulation raw data becomes more difficult and needs additional effort. More precisely, the gap between compute and I/O performance is widening with current supercomputers. Thus, the classical approach of visualizing simulation results in a post-processing step is limited or even impossible for extreme-scale scenarios. One promising technique to overcome this issue is in situ visualization, which visualizes and analyzes simulation data during simulation runtime. Within this work, in situ visualization using VisIt/Libsim has been added to the CIAO code framework for interactive- and batch-mode visualization on JUQUEEN, an IBM Blue Gene/Q system with 458 752 cores. Full-system runs are demonstrated and early results of performance measurements of an extreme-scale multiphase case are discussed.

Framework for Sharing of Highly Resolved Turbulence Simulation Data

The growing computational capabilities of nowadays supercomputers have made highly resolved turbulence simulations possible. The large data-sets and tremendous amount of required compute resources create serious new challenges when attempting to share the data between different research groups. But even more difficult to solve is the incompatibility of the data formats and numerical approaches used for turbulence simulations, which in detail are often only known to the simulation code developer. In this paper a framework for sharing data of large scale simulations is presented, which simplifies the access and further post-processing even beyond a single supercomputing center. It combines established services to provide an easy to manage-and-extend software setup without the need to standardize a database or -format. Beside other advantages, it enables the use of direct file outputs from simulation runs which are often archived anyway.

Performance Optimization of Parallel Applications in Diverse On-Demand Development Teams

Current supercomputing platforms and scientific application codes have grown rapidly in complexity over the past years. Multi-scale, multi-domain simulations on one hand and deep hierarchies in large-scale computing platforms on the other make it exceedingly harder to map the former onto the latter and fully exploit the available computational power. The complexity of the software and hardware components involved calls for in-depth expertise that can only be met by diversity in the application development teams. With its model of simulation labs and cross-sectional groups, JARA-HPC enables such diverse teams to form on demand to solve concrete development problems. This work showcases the effectiveness of this model with two application case studies involving the JARA-HPC cross-sectional group “Parallel Efficiency” and simulation labs and domain-specific development teams. For one application, we show the results of a completed optimization and the estimated financial impact of the combined efforts. For the other application, we present results from an ongoing engagement, where we show how an on-demand team investigates the behavior of dynamic load balancing schemes for an MD particle simulation, leading to a better overall understanding of the application and revealing targets for further investigation.