World Scientific
  • Search
Skip main navigation

Cookies Notification

We use cookies on this site to enhance your user experience. By continuing to browse the site, you consent to the use of our cookies. Learn More
×
Our website is made possible by displaying certain online content using javascript.
In order to view the full content, please disable your ad blocker or whitelist our website www.worldscientific.com.

System Upgrade on Tue, Oct 25th, 2022 at 2am (EDT)

Existing users will be able to log into the site and access content. However, E-commerce and registration of new users may not be available for up to 12 hours.
For online purchase, please visit us again. Contact us at [email protected] for any enquiries.

PARALLEL ADAPTIVE QUANTUM TRAJECTORY METHOD FOR WAVEPACKET SIMULATIONS

    Time-dependent wavepackets are widely used to model various phenomena in physics. One approach in simulating the wavepacket dynamics is the quantum trajectory method (QTM). Based on the hydrodynamic formulation of quantum mechanics, the QTM represents the wavepacket by an unstructured set of pseudoparticles whose trajectories are coupled by the quantum potential. The governing equations for the pseudoparticle trajectories are solved using a computationally-intensive moving weighted least squares (MWLS) algorithm, and the trajectories can be computed in parallel. This paper contributes a strategy for improving the performance of wavepacket simulations using the QTM. Specifically, adaptivity is incorporated into the MWLS algorithm, and loop scheduling techniques are employed to dynamically load balance the parallel computation of the trajectories. The adaptive MWLS algorithm reduces the amount of computations without sacrificing accuracy, while adaptive loop scheduling addresses the load imbalance introduced by the algorithm and the runtime system. Results of experiments on a Linux cluster are presented to confirm that the adaptive MWLS reduces the trajectory computation time by up to 24%, and adaptive loop scheduling achieves parallel efficiencies of up to 85% when simulating a free particle.

    References

    • D. Bohm, Physical Review 85, 166 (1952). Crossref, ISIGoogle Scholar
    • C. L. Lopreore and R. W. Wyatt, Physical Review Letters 82, 5190 (1999). Crossref, ISIGoogle Scholar
    • R. G. Brooket al., International Journal of Quantum Chemistry 85, 263 (2001). Crossref, ISIGoogle Scholar
    • R. L.   Carino and I.   Banicescu , Dynamic scheduling parallel loops with variable iterate execution times , Proceedings of the 16th International Parallel and Distributed Processing Symposium (IPDPS 2002) - Workshop on Parallel and Distributed Scientific and Engineering Applications . Google Scholar
    • C. Polychronopoulos and D. Kuck, IEEE Trans on Computers C-36, 1425 (1987). Crossref, ISIGoogle Scholar
    • S. Flynn Hummel, E. Schonberg and L. E. Flynn, Communications of the ACM 35, 90 (1992). Crossref, ISIGoogle Scholar
    • I.   Banicescu and V.   Velusamy , Performance of scheduling scientific applications with adaptive weighted factoring , Proceedings of the 15th International Parallel and Distributed Processing Symposium (IPDPS 2001) - Heterogeneous Computing Workshop . Google Scholar
    • I.   Banicescu and V.   Velusamy , Load balancing highly irregular computations with adaptive factoring , Proceedings of the 16th International Parallel and Distributed Processing Symposium (IPDPS 2002) - Heterogenous Computing Workshop . Google Scholar
    • S. Flynn Hummelet al., Load-sharing in heterogeneous systems Via weighted factoring, SPAA '96: Proceedings of the 8th Annual ACM Symposium on Parallel Algorithms and Architectures pp. 318–328. Google Scholar