Our knowledge of molecular biology and the machinery of life has been increasing in leaps and bounds. To coalesce this knowledge into a deeper understanding, we need to determine the structure of a multitude of proteins with high resolution, and understand the relationship between their structure and function. Molecular dynamics simulations help further this understanding by allowing us to observe dynamical phenomena occurring at an atomic level, and validate our understanding of the basic physical principles embodied in simulations. Simulations based on classical mechanics, with some approximations of the quantum-mechanical "reality'' are adequate for many situations; however, for simulations involving making and breaking of bonds, for example, a quantum mechanical simulation is necessary. The Car-Parinello algorithm and the ability to combine classical and quantum models in a single simulation are efficient ways of accomplishing this. In either case, the computational power needed for carrying out the simulations over an interesting interval of time of the biomolecular phenomena is so large that only parallel computers offer the hope of completing such simulations in a realistic time. Although large parallel computers are available now, it is quite challenging to parallelize the simulations so as to scale to thousands of processors and beyond. This paper presented an overview of strategies aimed at this problem, and presented in some detail the particular strategies the authors have been pursuing.
Laxmikant V. Kale and Klaus Schulten and Robert D. Skeel and Glenn Martyna and Mark Tuckerman and James C. Phillips and Sameer Kumar and Gengbin Zheng, "Biomolecular modeling using parallel supercomputers", Handbook of computational molecular biology, 2005. Editor S. Aluru, Publ: Taylor and Francis, pp. 34.1-34.43.