82,969 research outputs found
Task-based Augmented Contour Trees with Fibonacci Heaps
This paper presents a new algorithm for the fast, shared memory, multi-core
computation of augmented contour trees on triangulations. In contrast to most
existing parallel algorithms our technique computes augmented trees, enabling
the full extent of contour tree based applications including data segmentation.
Our approach completely revisits the traditional, sequential contour tree
algorithm to re-formulate all the steps of the computation as a set of
independent local tasks. This includes a new computation procedure based on
Fibonacci heaps for the join and split trees, two intermediate data structures
used to compute the contour tree, whose constructions are efficiently carried
out concurrently thanks to the dynamic scheduling of task parallelism. We also
introduce a new parallel algorithm for the combination of these two trees into
the output global contour tree. Overall, this results in superior time
performance in practice, both in sequential and in parallel thanks to the
OpenMP task runtime. We report performance numbers that compare our approach to
reference sequential and multi-threaded implementations for the computation of
augmented merge and contour trees. These experiments demonstrate the run-time
efficiency of our approach and its scalability on common workstations. We
demonstrate the utility of our approach in data segmentation applications
Parallel Mapper
The construction of Mapper has emerged in the last decade as a powerful and
effective topological data analysis tool that approximates and generalizes
other topological summaries, such as the Reeb graph, the contour tree, split,
and joint trees. In this paper, we study the parallel analysis of the
construction of Mapper. We give a provably correct parallel algorithm to
execute Mapper on multiple processors and discuss the performance results that
compare our approach to a reference sequential Mapper implementation. We report
the performance experiments that demonstrate the efficiency of our method
A Process Calculus for Dynamic Networks
In this paper we propose a process calculus framework for dynamic networks in which the network topology may change as computation proceeds. The proposed calculus allows one to abstract away from neighborhood-discovery computations and it contains features for broadcasting at multiple transmission ranges and for viewing networks at different levels of abstraction. We develop a theory of confluence for the calculus and we use the machinery developed towards
the verification of a leader-election algorithm for mobile ad hoc networks
Computing on Anonymous Quantum Network
This paper considers distributed computing on an anonymous quantum network, a
network in which no party has a unique identifier and quantum communication and
computation are available. It is proved that the leader election problem can
exactly (i.e., without error in bounded time) be solved with at most the same
complexity up to a constant factor as that of exactly computing symmetric
functions (without intermediate measurements for a distributed and superposed
input), if the number of parties is given to every party. A corollary of this
result is a more efficient quantum leader election algorithm than existing
ones: the new quantum algorithm runs in O(n) rounds with bit complexity
O(mn^2), on an anonymous quantum network with n parties and m communication
links. Another corollary is the first quantum algorithm that exactly computes
any computable Boolean function with round complexity O(n) and with smaller bit
complexity than that of existing classical algorithms in the worst case over
all (computable) Boolean functions and network topologies. More generally, any
n-qubit state can be shared with that complexity on an anonymous quantum
network with n parties.Comment: 25 page
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