We describe our experiences using Python with the SPaSM molecular dynamics code at Los Alamos National Laboratory. Originally developed as a large monolithic application for massively parallel processing systems, we have used Python to transform our application into a flexible, highly modular, and extremely powerful system for performing simulation, data analysis, and visualization. In addition, we describe how Python has solved a number of important problems related to the development, debugging, deployment, and maintenance of scientific software. 1 Background For the past 5 years, we have been developing a largescale physics code for performing molecular-dynamics simulations of materials. This code, SPaSM (Scalable Parallel Short-range Molecular-dynamics), was originally developed for the Connection Machine 5 massively parallel supercomputing system and later moved to a number of other machines including the Cray T3D, multiprocessor Sun and SGI systems, and Unix workstations [1, 2]....

Recent advancements in reactive molecular dynamics (MD) simulations based on many-body interatomic potentials necessitate efficient dynamic n-tuple computation, where a set of atomic n-tuples within a given spatial range is constructed at every time step. Here, we develop a computation-pattern algebraic framework to mathematically formulate general n-tuple computation. Based on translation/reflection-invariant properties of computation patterns within this framework, we design a shift-collapse (SC) algorithm for cell-based parallel MD. Theoretical analysis quantifies the compact n-tuple search space and small communication cost of SC-MD for arbitrary n, which are reduced to those in best pair-computation approaches (e.g. eighth-shell method) for n = 2. Benchmark tests show that SC-MD outperforms our production MD code at the finest grain, with 9.7-and 5.1-fold speedups on Intel-Xeon and BlueGene/Q clusters. SC-MD also exhibits excellent strong scalability.