Prof. Ahmed E. Ismail leads the Molecular Simulations and Transformations research group, which is affiliated with the Tailor-Made Fuels from Biomass Cluster of Excellence, the Aachener Verfahrenstechnik, and the AICES Graduate School at RWTH Aachen University. Prof. Ismail received his bachelor’s and doctorate in chemical engineering from Yale and MIT; after graduation, he was a postdoc and later technical staff member at Sandia National Laboratories. His research group performs large-scale molecular dynamics simulations for a number of applications, including biomass dissolution, interfacial structure and dynamics, and connections to coarse-grained methods.
Paolo Bientinesi is Professor for Algorithm-Oriented Code Generation for High-Performance Architectures in the Computer Science department at RWTH Aachen University; he leads the group High-Performance and Automatic Computing within the Aachen Institute for Computational Engineering Science (AICES). Prof. Bientinesi studied at the University of Pisa (MS, 1998) and at The University of Texas at Austin (Ph.D., 2006); he was a postdoctoral associate at Duke University prior to joining RWTH Aachen. His expertize lies in performance modeling, numerical linear algebra, and automation. He often collaborates with the European Commission as expert evaluator, and is the recipient of the 2009 Karl Arnold Prize from the North Rhine-Westphalian Academy of Sciences and Humanities.
Molecular dynamics is a highly compute-intensive simulation tool for determining the thermodynamic, kinetic, and transport properties of various materials. The rise of affordable multicore workstations and parallel computing clusters has led to the widespread proliferation of molecular dynamics as a tool through almost all areas of science and engineering, with applications as diverse as protein folding and aggregation to nanoparticle rheology in industrial processing to materials for nuclear waste storage and carbon capture. In spite of the increasing availability of various accelerator hardware, molecular dynamics codes have been not kept pace, thereby preventing codes from taking full advantage of the latest generation of hardware, including the Intel® Xeon Phi™ coprocessors.
The goal of our Intel® Parallel Computing Center is to optimize some of the most important computational kernels in the LAMMPS molecular dynamics package for Intel® architecture. One area which we will address in our work is the problem of multi body potentials, which go beyond the standard pairwise potentials normally used and incorporate three-body, four-body, and sometimes many-body interactions as well. Two such methods that are frequently used in the literature are the AIREBO and Tersoff potentials, which are encountered in simulations of carbon nano tubes, graphene, and other hydrocarbons. By changing how neighbor lists are stored, and by rearranging the data used by the calculations, we will better exploit the intranode parallelism and vectorization capabilities of the Intel® Xeon Phi™ coprocessor. The most critical part of our work will focus on the long-range solvers present in LAMMPS. In many large-scale molecular simulations, these solvers, which are responsible for calculating the forces resulting from either electrostatic or dispersion forces, represent the largest computational bottleneck, often accounting for as much as 90 to 95 percent of the total computational expense of a simulation. Consequently, any efficiency gains achieved in these algorithms can have a major impact on the MD community at large. We will exploit the built-in vectorization capabilities of the Intel® Xeon Phi™ coprocessor by adjusting how data is packed into arrays that handle particle mapping as well as the Poisson solver routines in the particle-particle particle-mesh (PPPM) algorithms in LAMMPS. We will also explore possible improvements in the API which connects LAMMPS to the underlying Fast Fourier transform solvers that drive the PPPM algorithm.
- February 14, 2018, Parallel Programming, HPAC
- Markus Hohnerbach, Ahmed E. Ismail, Paolo Bientinesi, November 16, 2015, Tersoff many-body potential: Sustainable performance thru vectorization, SC15 Workshop: Producing High Perf and Sustainable SW for Molecular Simulations
- Intel® Parallel Computing Center - RWTH Aachen
- High-Performance and Automatic Computing HPAC
- High-Performance and Automatic Computing GitHub*
- A Vectorized Implementation of the Tersoff Potential for the LAMMPS Molecular Dynamics Software GitHub
- Implementation of the Buckingham potential for Intel® Xeon Phi™ coprocessors GitHub