2,644 research outputs found
Parallel Algorithm for Solving Kepler's Equation on Graphics Processing Units: Application to Analysis of Doppler Exoplanet Searches
[Abridged] We present the results of a highly parallel Kepler equation solver
using the Graphics Processing Unit (GPU) on a commercial nVidia GeForce 280GTX
and the "Compute Unified Device Architecture" programming environment. We apply
this to evaluate a goodness-of-fit statistic (e.g., chi^2) for Doppler
observations of stars potentially harboring multiple planetary companions
(assuming negligible planet-planet interactions). We tested multiple
implementations using single precision, double precision, pairs of single
precision, and mixed precision arithmetic. We find that the vast majority of
computations can be performed using single precision arithmetic, with selective
use of compensated summation for increased precision. However, standard single
precision is not adequate for calculating the mean anomaly from the time of
observation and orbital period when evaluating the goodness-of-fit for real
planetary systems and observational data sets. Using all double precision, our
GPU code outperforms a similar code using a modern CPU by a factor of over 60.
Using mixed-precision, our GPU code provides a speed-up factor of over 600,
when evaluating N_sys > 1024 models planetary systems each containing N_pl = 4
planets and assuming N_obs = 256 observations of each system. We conclude that
modern GPUs also offer a powerful tool for repeatedly evaluating Kepler's
equation and a goodness-of-fit statistic for orbital models when presented with
a large parameter space.Comment: 19 pages, to appear in New Astronom
Stitching IC Images
Image stitching software is used in many areas such as photogrammetry, biomedical imaging, and even amateur digital photography. However, these algorithms require relatively large image overlap, and for this reason they cannot be used to stitch the integrated circuit (IC) images, whose overlap is typically less than 60 pixels for a 4096 by 4096 pixel image.
In this paper, we begin by using algorithmic graph theory to study optimal patterns for adding IC images one at a time to a grid. In the remaining sections we study ways of stitching all the images simultaneously using different optimisation approaches: least squares methods, simulated annealing, and nonlinear programming
ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R
We introduce the C++ application and R package ranger. The software is a fast
implementation of random forests for high dimensional data. Ensembles of
classification, regression and survival trees are supported. We describe the
implementation, provide examples, validate the package with a reference
implementation, and compare runtime and memory usage with other
implementations. The new software proves to scale best with the number of
features, samples, trees, and features tried for splitting. Finally, we show
that ranger is the fastest and most memory efficient implementation of random
forests to analyze data on the scale of a genome-wide association study
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