17129

A Domain Specific Language for Performance Portable Molecular Dynamics Algorithms

William R. Saunders, James Grant, Eike H. Muller
University of Bath, Bath BA2 7AY, Bath, United Kingdom
arXiv:1704.03329 [cs.DC], (11 Apr 2017)

@article{saunders2017domain,

   title={A Domain Specific Language for Performance Portable Molecular Dynamics Algorithms},

   author={Saunders, William R. and Grant, James and Muller, Eike H.},

   year={2017},

   month={apr},

   archivePrefix={"arXiv"},

   primaryClass={cs.DC}

}

Developers of Molecular Dynamics (MD) codes face significant challenges when adapting existing simulation packages to new hardware. In a continuously diversifying hardware landscape it becomes increasingly difficult for scientists to be experts both in their own domain (physics/chemistry/biology) and specialists in the low level parallelisation and optimisation of their codes. To address this challenge, we describe a "Separation of Concerns" approach for the development of parallel and optimised MD codes: the science specialist writes code at a high abstraction level in a domain specific language (DSL), which is then translated into efficient computer code by a scientific programmer. In a related context, an abstraction for the solution of partial differential equations with grid based methods has recently been implemented in the (Py)OP2 library. Inspired by this approach, we develop a Python code generation system for molecular dynamics simulations on different parallel architectures, including massively parallel distributed memory systems and GPUs. We demonstrate the efficiency of the auto-generated code by studying its performance and scalability on different hardware and compare it to other state-of-the-art simulation packages. With growing data volumes the extraction of physically meaningful information from the simulation becomes increasingly challenging and requires equally efficient implementations. A particular advantage of our approach is the easy expression of such analysis algorithms. We consider two popular methods for deducing the crystalline structure of a material from the local environment of each atom, show how they can be expressed in our abstraction and implement them in the code generation framework.
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