Nanostructure modeling is the computation of the positions
and orbitals of atoms in arbitrary nanostructures.
Accurate atomic-scale quantum theory of nanostructures and nanosystems
fabricated from nanostructures enables precision metrology of these
nanosystems and provides the predictive, precision modeling tools
needed for engineering these systems for applications including advanced
semiconductor lasers and detectors, single photon sources and
detectors, biosensors, and nanoarchitectures for quantum coherent
technologies such as quantum computing. The tight-binding
model based upon the Linear Combination of Atomic
Orbitals (LCAO) method provides an accurate atomistic theory for nanostructures.
The nanostructure modeling code has been parallelized.
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Why Parallelize Nanostructure Modeling?
The
tight-binding method is ideal for modeling small
nanostructures. However, for modeling nanostructures
with more than 25,000 atoms, the method is impractical
on sequential computers due to long run times. Significant improvements
in run time can be achieved through parallelization.
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How is the Parallelization Realized?
There are two parts to parallelizing this problem: creating the
structure; and solving the Hamiltonian equation.
The structure is created geometrically. We parallelize
this by dividing the
structure into layers. Communication
is across layers. The starting point is a cubic structure that encompasses
the desired nanostructure; the structure shape is created by pruning away the
excess.
We parallelize solving the Hamiltonian with PARPACK.
The parallel implementation can handle arbitrary
nanostructure shapes through an input file specification procedure.
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What is the Performance of the Parallel Code?
We ran the code on the NIST NBS Cluster of 500Mhz Pentium III processors.
Each processor has a Gigabyte of memory.
For the structure consisting of three concentric spheres with diameters 3, 4, and 5
lattice units, the timing data closely matches
the formula: T = 655.7 + 3116.0/N.
T is execution time (in seconds), and N is the number of processors.
The non-parallelizable computation time is 655.7 seconds;
while the parallelizable portion of the computation uses 3116.0 seconds.
Thus the portion of the code that was directly parallelizable with
PARPACK is almost 83%.
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Modeling Quantum Dots |
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Papers/Presentations
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James S. Sims, William L. George, Steven G. Satterfield, Howard K. Hung, John G. Hagedorn, Peter M. Ketcham, Terence J. Griffin, Stanley A. Hagstrom, Julien C. Franiatte, Garnett W. Bryant, W. Jaskolski, Nicos S. Martys, Charles E. Bouldin, Vernon Simmons, Olivier P. Nicolas, James A. Warren, Barbara A. am Ende, John E. Koontz, B. James Filla, Vital G. Pourprix, Stefanie R. Copley, Robert B. Bohn, Adele P. Peskin, Yolanda M. Parker and Judith E. Devaney, Accelerating Scientific Discovery Through Computation
and Visualization II,
NIST Journal of Research, 107
(3)
,
May-June, 2002,
pp. 223-245.
Links:
postscript and pdf.
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Julien C. Franiatte, Steven G. Satterfield, Garnett W. Bryant and Judith E. Devaney,
Parallelization and Visualization of Computational Nanotechnology LCAO
Method
delivered at Nanotechnology at the Interface of Information Technology, New Orleans, LA,
February 7-9, 2002.
Links:
pdf and pdf.
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Garnett W. Bryant, J. Aizpurua, Rui-Hui Xie, Julien C. Franiatte, Judith E. Devaney, W. Jaskolski, M. Zielinski, S. Lee, J. Kim, L. Jonsson and J. W. Wilkins,
Designing the Nanoworld: Atomic Scale Simulations of Nanostructures and Nanodevices
delivered at NIST Nanotechnology Open House, Gaithersburg, MD,
June 20, 2002.
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Julien C. Franiatte, Judith E. Devaney, Garnett W. Bryant, Steven G. Satterfield and William L. George,
Building Nanostructures Interactively in an Immersive Visualization Environment
delivered at NIST Nanotechnology Open House, Gaithersburg, MD,
June 20, 2002.
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