Massively parallel computer systems

The ultimate goal of Feller and Jordan s work is to produce a potential that accurately represents the interaction of water with a carbon nanotube. Their next study will involve HjO interacting with a cluster model of a carbon nanotube. However, this will require access to the next generation massively parallel computing system from Hewlett-Packard that is currendy being installed in EMSL s Molecular Science Computing Facility. " ... 

A novel approach to protein conformation is the entropy-sampling Monte Carlo method (ESMC), which is described in detail in another contribution to this volume. The method provides a complete thermodynamic description of protein models, but it is computationally quite expensive. However, because of the underlying data-parallel structure of ESMC algorithms, computations could be done on massively parallel computers essentially without the communication overhead typical for the majority of other simulation techniques. This technique will undoubtedly be applied to numerous systems in the near future. 

Future research efforts relating to molecular dynamics simulation in micro- and nanofluidics could be devoted to two areas, i. e., development of the simulation methodology itself and fundamental studies of the phenomena in micro- and nanofluidics. With the advent of massively parallel computers, it is possible to increase both the length scale and the time scale of molecular dynamics simulations to model larger and more complex systems. However, the current parallel molecular dynamics codes do not scale well enough to use thousands of processors provided by modem computer clusters. Therefore, parallel algorithms with better scalability are highly desirable. 

One advantage of the bond fluctuation method is that it works well on a variety of platforms, including vector computers, massively parallel computers, and super scalar processors. For a dense polymer melt in three dimensions, the vectorized code gives ca. 1.7 x 10 attempted moves per second for one processor on the Cray YMP for a volume fractions of (f) = 0.41. On massively parallel computers, the program has only been run to date in such a way that each processor ran one independent system. While the method should be parallelizable across multiple processors, this has not been done yet. 

The theory and applications of CAs and similar systems will likely attract increasing interest as the capabilities for massive parallel computation expand, and robust, decentralized, and highly distributed systems become imperative. CAs and similar systems will also be increasingly important as modeling tools, as scientists probe more deeply into the behavior of natural systems composed of simple components with local communication and emergent collective properties. I hope that this review will contribute to the increase of interest in CAs and to the better understanding and wider application of such systems. 

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