Abstract
Molecular Dynamics is an important tool for computational biologists, chemists, and materials scientists, consuming a sizable amount of supercomputing resources. Many of the investigated systems contain charged particles, which can only be simulated accurately using a long-range solver, such as PPPM. We extend the popular LAMMPS molecular dynamics code with an implementation of PPPM particularly suitable for the second generation Intel Xeon Phi. Our main target is the optimization of computational kernels by means of vectorization, and we observe speedups in these kernels of up to 12\(\times \). These improvements carry over to LAMMPS users, with overall speedups ranging between 2–3\(\times \), without requiring users to retune input parameters. Furthermore, our optimizations make it easier for users to determine optimal input parameters for attaining top performance.
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The authors gratefully acknowledge financial support from the Deutsche Forschungsgemeinschaft (German Research Association) through grant GSC 111, and from Intel Corporation via the Intel Parallel Computing Center initiative.
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McDoniel, W., Höhnerbach, M., Canales, R., Ismail, A.E., Bientinesi, P. (2017). LAMMPS’ PPPM Long-Range Solver for the Second Generation Xeon Phi. In: Kunkel, J.M., Yokota, R., Balaji, P., Keyes, D. (eds) High Performance Computing. ISC High Performance 2017. Lecture Notes in Computer Science(), vol 10266. Springer, Cham. https://doi.org/10.1007/978-3-319-58667-0_4
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DOI: https://doi.org/10.1007/978-3-319-58667-0_4
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