Routing, merging, and sorting on parallel models of computation
Journal of Computer and System Sciences
Efficient plane sweeping in parallel
SCG '86 Proceedings of the second annual symposium on Computational geometry
Efficient parallel solutions to some geometric problems
Journal of Parallel and Distributed Computing
Parallel processing for efficient subdivision search
SCG '87 Proceedings of the third annual symposium on Computational geometry
The Parallel Evaluation of General Arithmetic Expressions
Journal of the ACM (JACM)
On Finding the Maxima of a Set of Vectors
Journal of the ACM (JACM)
Lower bounds for algebraic computation trees
STOC '83 Proceedings of the fifteenth annual ACM symposium on Theory of computing
Efficient parallel techniques for computational geometry
Efficient parallel techniques for computational geometry
An Optimal Worst Case Algorithm for Reporting Intersections of Rectangles
IEEE Transactions on Computers
Parallel computational geometry
SFCS '85 Proceedings of the 26th Annual Symposium on Foundations of Computer Science
An optimal parallel algorithm for integer sorting
SFCS '85 Proceedings of the 26th Annual Symposium on Foundations of Computer Science
SFCS '86 Proceedings of the 27th Annual Symposium on Foundations of Computer Science
An Improved Output-size Sensitive Parallel Algorithm for Hidden-Surface Removal for Terrains
IPPS '98 Proceedings of the 12th. International Parallel Processing Symposium on International Parallel Processing Symposium
Short Communication: A faster optimal algorithm for the measure problem
Parallel Computing
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We present techniques for parallel divide-and-conquer, resulting in improved parallel algorithms for a number of problems. The problems for which we give improved algorithms include intersection detection, trapezoidal decomposition (hence, polygon triangulation), and planar point location (hence, Voronoi diagram construction). We also give efficient parallel algorithms for fractional cascading, 3-dimensional maxima, 2-set dominance counting, and visibility from a point. All of our algorithms run in O(log n) time with either a linear or sub-linear number of processors in the CREW PRAM model.