Affiliations: Mechanical Engineering Department, The City College of
The City University of New York, Convent Ave. at 140th St., New York, NY 10031,
USA. Tel.: +1 212 650 5210; Fax: +1 815 572 8203; E-mail:
[email protected] | Mechanical Engineering Department, The Graduate Center
of The City University of New York, Fifth Ave. at 34th St., New York, NY 10015,
USA. E-mail: [email protected]
Note: [] Corresponding author. Current address: US Naval Research
Laboratory, 4555 Overlook Ave. SW, Washington, DC 20375, USA
Note: [] Current address: Mechanical Engineering Department, University
of California at Los Angeles, Los Angeles, CA 90095, USA
Abstract: The information hiding philosophy of object-oriented programming
encourages localizing data structures within objects rather than sharing data
globally across different classes of objects. This emphasis on local data leads
naturally to fine-grained data abstractions, particularly in scientific
simulations involving large collections of small, discrete physical or
mathematical objects. This paper focuses on a subset of such simulations where
dynamically reconfigurable links bind the objects together. It is demonstrated
that fine-grained data structures reduce the complexity of local operations on
the data at the potential expense of increased global operation complexity. Two
metrics are used to describe data structures: granularity is the number
of instantiations required to cover the data space, whereas extent is the
continuously traversable length of the data along a given direction. These
definitions are applied to two abstractions for simulating the turbulent motion
of quantum vortices in superfluid liquid helium. Several local and global
operations on a fine-grained linked list are compared with those on a
coarse-grained array. It is demonstrated that fine-grained data structures
recover the simplicity of more coarse-grained structures if maximal extent is
maintained as the granularity increases.
