We present a computational steering approach for controlling, analyzing, and visualizing very large scale molecular dynamics simulations involving tens to hundreds of millions of atoms. Our approach relies on extensible scripting languages and an easy to use tool for building extensions and modules. The system is easy to modify, works with existing C code, is memory efficient, and can be used from inexpensive workstations over standard Internet connections. We demonstrate how we have been able to explore data from production MD simulations involving as many as 104 million atoms running on the CM-5 and Cray T3D. We also show how this approach can be used to integrate common scripting languages (including Python, Tcl/Tk, and Perl), simulation code, user extensions, and commercial data analysis packages.

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]....