Carbon nanotubes are expected to play a significant role in the design and manufacture of many nano-mechanical and nano-electronic devices of future. It is important, therefore, that atomic level elastomechanical response properties of both single and multiwall nanotubes be investigated in detail. Classical molecular dynamics simulations employing Brenner's reactive potential with long range van der Waals interactions have been used in mechanistic response studies of carbon nanotubes to external strains. The studies of single and multiwalled carbon nanotubes under compressive strains show the instabilities beyond elastic response. Due to inclusion of non-bonded long range interactions, the simulations also show the redistribution of strain and strain energy from sideways bucklng to the formation of highly localized strained kink sites. Bond rearrangements occur at the kink sites, leading to formation of topological defects, preventing the tube from relaxing fully back to it's original configuration. Elastomechanic response behavior of single and multiwall carbon nanotubes to externally applied compressive strains is simulated and studied in detail. We will describe the results and discuss their implication towards the stability of any molecular mechanical structure made of carbon nanotubes.

The design of a parallel implementation of multilevel recursive spectral bisection on the Cray T3D is described. The code is intended to be fast enough to enable dynamic repartitioning of adaptive meshes and to partition meshes that are too large for workstations. Two innovations in the implementation are recursive asynchronous task teams and a parallel version of the multilevel accelerator. A performance improvement of a factor of 140 over the best available serial implementation is demonstrated.

Abstract If problems involving unstructured meshes are to be solved efficiently on distributed‐memory parallel computers, the meshes must be partitioned and distributed across processors in a way that balances the computational load and minimizes communication. The recursive spectral bisection method (RSB) has been shown to be very effective for such partitioning problems compared to alternative methods, but RSB in its simplest form is expensive. Here a multilevel version of RSB is introduced that attains about an order‐of‐magnitude improvement in run time on typical examples.

A new algorithm for reducing the envelope of a sparse matrix is presented.This algorithm is based on the computation of eigenvectors of the Laplacian matrix associated with the graph of the sparse matrix.A reordering of the sparse matrix is determined based on the numerical values of the entries of an eigenvector of the Laplacian matrix.Numerical results show that the new reordering algorithm can in some cases reduce the envelope by more than a factor of two over the current standard algorithms such as Gibbs-Poole-Stockmeyer (GPS) or SPA RSPAK'S reverse Guthil!-McKee (RCM).Permission to copy witiout fce all or w of thts mmenal IS granted, provided tha! the copies am not made or disinbuted for dmct commercial advantaga, fhe ACM copy 'ight nofme and !he litlc of the pablicahon and 493 im dale appear, md notice is given Ibm copying is by pmnussion of lhe Association for Con,puung Maciinmy To cops ehenvise.or to republish.requwcs n fse mdhx specific permisaon

The authors describe the novel spectral nested dissection (SND) algorithm, a novel algorithm for computing orderings appropriate for parallel factorization of sparse, symmetric matrices. The algorithm makes use of spectral properties of the Laplacian matrix associated with the given matrix to compute separators. The authors evaluate the quality of the spectral orderings with respect to several measures: fill, elimination tree height, height and weight balances of elimination trees, and clique tree heights. They use some very large structural analysis problems as test cases and demonstrate on these real applications that spectral orderings compare quite favorably with commonly used orderings, outperforming them by a wide margin for some of these measures. The only disadvantage of SND is its relatively long execution time.>

An algorithm for matching images of real world scenes is presented. The matching is a specification of the geometrical disparity between the images and may be used to partially reconstruct the three-dimensional structure of the scene. Sets of candidate matching points are selected independently in each image. These points are the locations of small, distinct features which are likely to be detectable in both images. An initial network of possible matches between the two sets of candidates is constructed. Each possible match specifies a possible disparity of a candidate point in a selected reference image. An initial estimate of the probability of each possible disparity is made, based on the similarity of subimages surrounding the points. These estimates are iteratively improved by a relaxation labeling technique making use of the local continuity property of disparity that is a consequence of the continuity of real world surfaces. The algorithm is effective for binocular parallax, motion parallax, and object motion. It quickly converges to good estimates of disparity, which reflect the spatial organization of the scene.