Background & Project Description

Current implementations of first principles electronic structure methods for molecules, clusters, local models of surfaces and solids do not admit a homogeneous parallelization strategy and often suffer from scalability problems. This, in turn, severely limits opportunities in modelling of chemical aspects of complex systems like catalysts, nano-structured materials as well as large complexes in solution.

The project will develop a new paradigm for the parallelization of density functional theory (DFT) methods for electronic structure calculations. Advanced embedding techniques will account for environment effects (e.g. solvent, support). Pertinent demonstration examples will derive from application projects of the coordinator (Rösch), e.g. transition metal nano particles, their reactivity and sorption on support, heterogeneous and homogeneous catalysis, and large metal complexes in aqueous medium. Finally the new quality of simulation performance will be showcased with “real-life” applications from these areas.

For software layout and low-level numerical methods the project relies on the well established software ParaGauss developed by the coordinator. The project aims at a strong modularization of the Kohn-Sham approach to DFT, facilitating task-specific parallelization, memory management, and low-level optimization. Beyond a fundamental restructuring of the software ParaGauss, development of a direct “exact exchange” module for energy and forces is planned.

The project benefits from extensive experience in developing quantum chemistry methods (Rösch). This expertise will be combined with mathematical and computer science expertise of the partners. The partners will closely collaborate at the design stage, for algorithm development (Rösch, Bungartz), and when accounting for hardware aspects and parallelisation (Gerndt, Bode). LRZ contributes optimization and performance analysis on HPC platforms such as HLRB 2 (Bode, Hegering).

MAC — Munich Centre of Advanced Computing

Gold nanoparticle Au55Cl6(PH3)12

Project: B6 – Efficient Parallel Strategies in Computational Modelling of Materials

Principal Investigators

Prof. Dr. Dr. h.c. Notker Rösch (Theoretical Chemistry, TUM)Prof. Dr. Arnd Bode (Parallel Architectures, TUM and LRZ)Prof. Dr. Hans-Joachim Bungartz (Scientific Computing, TUM)Prof. Dr. Michael Gerndt (Parallel Architectures, TUM)Prof. Dr. Heinz-Gerd Hegering (Supercomputing, LRZ)

Aspects of current work

1 Scaling on Massively Parallel ArchitecturesEfficient analytic calculation of Hamilton matrix and Coulomb integrals is essential for performance● Grouping batches of integrals for parallel execution: load balancing vs. data locality● Performance analysis tools: Scalasca and Vampir

2 Parallel Solver for Blocked Eigenvalue ProblemRepeated diagonalization of Hamiltonmatrix in the SCF procedure● Parallel eigensolvers (ScaLAPACK)efficiently handle large matrices● Scheduling algorithm allocates resources for subtasks of blocked eigenvalue problem● Better scalability and shorter runtime

3 Parallel Search on Potential Energy SurfacesImplementing and parallelizing state-of-the-art transition state searching methods● Global search algorithm operates withseveral conformations● Multiple intermediate path points are analyzed concurrently● Each conformation processed by generic quantum chemical program package

4 Modular Framework, Resource ManagementDedicated modules for subtasks using optimal parallelization strategy, dynamic resource allocation and scheduling● Modules for quantum-mechanical energy evaluation and forces● Geometry optimization and transition state search modules● High-level Python driver framework for task control and scheduling

5 Scalable Two-Electron Integral Package● Optimization strategies for serial calculation of "integral classes": efficient approaches for integral computation, screening, full exploitation of symmetry, storage of converged KS-matrix elements● Parallelization strategies: cost estimate for presorting subtasks, various dynamic scheduling algorithms for load balancing