NASA Scientific and Technical Aerospace Reports

covariates. The broad objective of this report is to focus on this strategy of modeling the differences between studies, by
comparing and contrasting several meta-regression methods.
NTIS
Heterogeneity; Regression Analysis
20040071047 Lawrence Livermore National Lab., Livermore, CA
Parallelizing a High Accuracy Hardware-Assisted Volume Renderer for Meshes with Arbitrary Polyhedra
Bennett, J.; Cook, R.; Max, N.; May, D.; Williams, P.; Mar. 26, 2001; In English
Report No.(s): DE2004-15005664; UCRL-JC-143127; No Copyright; Avail: National Technical Information Service (NTIS)
This paper discusses the authors efforts to improve the performance of the high-accuracy (HIAC) volume rendering
system, based on cell projection, which is used to display unstructured, scientific data sets for analysis. The parallelization of
HIAC, using the pthreads and MPI API’s, resulted in significant speedup, but interactive frame rates are not yet attainable for
very large data sets.
NTIS
Polyhedrons; Hardware; Computational Grids; Accuracy
20040071058 Forest Products Lab., Madison, WI
Estimating the Board Foot to Cubic Foot Ratio
Verrill, S.; Herian, V. L.; Spelter, H.; 2004; 24 pp.; In English
Report No.(s): PB2004-105070; FPL-RP-616; No Copyright; Avail: CASI; A03, Hardcopy
Certain issues in recent soft-wood lumber trade negotiations have centered on the method for converting estimates of
timber volumes reported in cubic meters to board feet. Such conversions depend on many factors; three of the most important
of these are log length, diameter, and taper. Average log diameters vary by region and have declined in the western USA due
to the growing scarcity of large diameter, old-growth trees. Such a systematic reduction in size in the log population affects
volume conversions from cubic units to board feet, which makes traditional rule of thumb conversion factors antiquated. In
this paper, we present an improved empirical method for performing cubic volume to board foot conversion.
NTIS
Estimates; Populations; Wood
20040071094 Lawrence Livermore National Lab., Livermore, CA
Multivariate Clustering of Large-Scale Scientific Simulation Data
Eliassi-Rad, T.; Critchlow, T.; Jun. 13, 2003; In English
Report No.(s): DE2004-15005754; UCRL-JC-153698; No Copyright; Avail: National Technical Information Service (NTIS)
Simulations of complex scientific phenomena involve the execution of massively parallel computer programs. These
simulation programs generate large-scale data sets over the spatio-temporal space. Modeling such massive data sets is an
essential step in helping scientists discover new information from their computer simulations. In this paper, we present a
simple but effective multivariate clustering algorithm for large-scale scientific simulation data sets. Our algorithm utilizes the
cosine similarity measure to cluster the field variables in a data set. Field variables include all variables except the spatial (x,
y, z) and temporal (time) variables. The exclusion of the spatial dimensions is important since ‘similar’ characteristics could
be located (spatially) far from each other. To scale our multivariate clustering algorithm for large-scale data sets, we take
advantage of the geometrical properties of the cosine similarity measure. This allows us to reduce the modeling time from
O(n(sup 2)) to O(n x g(f(u))), where n is the number of data points, f(u) is a function of the user-defined clustering threshold,
and g(f(u)) is the number of data points satisfying f(u). We show that on average g(f(u)) is much less than n. Finally, even
though spatial variables do not play a role in building clusters, it is desirable to associate each cluster with its correct spatial
region. To achieve this, we present a linking algorithm for connecting each cluster to the appropriate nodes of the data set’s
topology tree (where the spatial information of the data set is stored). Our experimental evaluations on two large-scale
simulation data sets illustrate the value of our multivariate clustering and linking algorithms.
NTIS
Algorithms; Data Simulation
20040073487 NASA Langley Research Center, Hampton, VA, USA
Discontinuous Galerkin Finite Element Method for Parabolic Problems
Kaneko, Hideaki; Bey, Kim S.; Hou, Gene J. W.; [2004]; 19 pp.; In English
Contract(s)/Grant(s): NAG1-2300; NAG1-01092; No Copyright; Avail: CASI; A03, Hardcopy
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