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TY  - THES
AU  - Malapally, Nitin
TI  - Exascale-ready molecular dynamics simulations with efficient algorithms for extreme core counts
PB  - RWTH Aachen University
VL  - Dissertation
CY  - Aachen
M1  - RWTH-2026-00768
SP  - 1 Online-Ressource : Illustrationen
PY  - 2026
N1  - Veröffentlicht auf dem Publikationsserver der RWTH Aachen University 2026
N1  - Dissertation, RWTH Aachen University, 2026
AB  - Biomolecular simulations, realized by molecular dynamics (MD) and enhanced-sampling approaches, are very powerful tools for studying the structural dynamics, kinetics, and energetics of biological systems. In combination with high-performance computing (HPC), increasingly larger systems and longer timescales can be simulated. However, the sequential nature of MD’s time evolution imposes a hard parallel limit, resulting in reduced scalability and hence under-utilization of HPC systems. As a result, standard MD does not reach the typical timescale (millisecond and beyond) required to study many biological processes. Enhanced-sampling techniques, such as umbrella sampling, metadynamics, and replica-exchange MD, do simulate these long processes but often require various techniques to retrieve kinetic and thermodynamic properties and do not scale well. The arrival of exascale computers has made the need for highly scalable algorithms for MD simulations even more urgent. This doctoral thesis reports on my efforts to address these important issues via algorithmic optimization, design and development. The first was an attempt to speed up MD simulations by means of an alternative parallel 3D discrete Fourier transform (3D DFT) algorithm, which was implemented and benchmarked on the JUWELS Cluster, showing comparable scaling performance to the state-of-the-art. In the second, the software apparatus required for a massively parallel MD strategy was constructed within the highly popular GROMACS code and the PLUMED library. The implementation is capable of both multi-CPU and multi-GPU parallelism and was optimized and benchmarked on the JUWELS Booster. The results revealed its multi-modal scalability in that simulations using it can be both efficiently sped up and also greatly extended for a small increase in runtime. The implementation was shown to scale up to 94
LB  - PUB:(DE-HGF)11
DO  - DOI:10.18154/RWTH-2026-00768
UR  - https://publications.rwth-aachen.de/record/1026256
ER  -