Advances in GPU architecture have made efficient implementations of hash
tables possible, allowing fast parallel constructions and retrievals despite the
uncoalesced memory accesses naturally incurred by hashing algorithms. The key is
to mitigate the penalty of these accesses by minimizing the number that occur and
utilizing the cache (when one is available). Most work done on parallel hashing is
ill-equipped for this objective and relies on the theoretical PRAM model, which
abstracts away the difficulties of programming on actual hardware. We examine
multiple hashing schemes from a practical perspective using NVIDIA's CUDA architecture.
We demonstrate an efficient data-parallel algorithm for building
large hash tables of millions of elements in real-time. We combine
two parallel algorithms for the construction: a classical sparse perfect
hashing approach, and cuckoo hashing, which packs elements densely by
allowing an element to be stored in one of multiple possible locations.
We introduce a volumetric space-time technique for the reconstruction
of moving and deforming objects from point data.
of our method is a four-dimensional space-time solid, made up of
spatial slices, each of which is a three-dimensional solid bounded
by a watertight manifold.
We introduce Localized Components Analysis (LoCA) for describing surface shape variation in an ensemble of biomedical objects using a linear subspace of spatially localized shape components.
In contrast to earlier methods, LoCA optimizes explicitly for localized components and allows a flexible trade-off between localized and concise representations.
Given geometric computer models of
anatomical shapes for some collection of specimens - here the
skulls of the some of the extant members of a family of monkeys
- an evolutionary tree for the group implies a hypothesis about the
way in which the shape changed through time.
We introduce a technique to visualize the gradual evolutionary
change of the shapes of living things as a morph between known
You've stumbled upon the website of Dan Anthony Alcantara, a Ph.D graduate
of the UC Davis Computer Science department.