Computational Chemical Engineering on a Dedicated Beowulf Cluster

Carnegie Mellon University, Pittsburgh PA

Investigators

Abstract

ABSTRACT PI: David S. Sholl, Lorenz T. Biegler and Steinar Hauan Institution: Carnegie Mellon University Proposal Number: 0094407 This is an equipment grant to provide funds to purchase a 32 node Beowulf cluster to support research in advanced computing to be used by three research groups at Carnegie Mellon University. Beowulf clusters represent an advantageous architecture for advanced computing: they are inexpensive to construct, flexible to configure, and provide a powerful computing environment for a broad variety of scientific computing tasks. The three research groups that will share the cluster will be conducting research on molecular dynamics and computational chemistry, modeling and visualization to support process synthesis and large-scale discrete and continuous process optimization. The cluster will thus serve computations that require high peak performance on time scales of minutes and hours, along with sustained computational performance on time scales of days. These complementary profiles mean that by sharing the cluster, its capabilities will be used more fully than they would be by any group individually. The cluster can also easily be operated in a way that distributes resources between different usage modes without significant computational overhead. The cluster will initially increase the PIs' computational capabilities by about a factor of three and will be expandable over succeeding years and will increase their computing power by over an order of magnitude within the next five years, without rendering existing nodes obsolete. Specifically, the research of the three groups consists of the development of new algorithms for: Calculation in computational chemistry, particularly in molecular dynamics and Monte Carlo simulations to determine macroscopic material properties from details of atomic-scale structure; Accurate process modeling for process design and synthesis that includes phase equilibrium, representation and evaluation of process alternatives and visualization of design insights; and Optimization of large-scale steady state and dynamic processes that involve both discrete and continuous decisions along with advanced decomposition strategies.