Computational geometry: an introduction
Computational geometry: an introduction
Memory requirements for balanced computer architectures
ISCA '86 Proceedings of the 13th annual international symposium on Computer architecture
Scan line array processors for image computation
ISCA '86 Proceedings of the 13th annual international symposium on Computer architecture
Warp architecture and implementation
ISCA '86 Proceedings of the 13th annual international symposium on Computer architecture
Data movement techniques for the pyramid computer
SIAM Journal on Computing
Optimal Graph Algorithms on a Fixed-Size Linear Array
IEEE Transactions on Computers
P3E: New life for projection—based image processing
Journal of Parallel and Distributed Computing
Information Transfer in Distributed Computing with Applications to VLSI
Journal of the ACM (JACM)
The Design and Analysis of Computer Algorithms
The Design and Analysis of Computer Algorithms
Computational Aspects of VLSI
Efficient Image Processing Algorithms on the Scan Line Array Processor
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finding connected components on a scan line array processor
Proceedings of the seventh annual ACM symposium on Parallel algorithms and architectures
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Linear arrays are characterized by a small communication bandwidth and a large communication diameter rendering them unsuited to the implementation of global computations. This paper presents efficient data movement and partitioning techniques to overcome several shortcomings of linear arrays. These techniques are used to derive optimal parallel algorithms for several geometric problems on n × n images using a fixed-size linear array with p processors, where 1 ≤ p ≤ n. O(n2/p) time solutions are presented for labeling connected image regions, computing the convex hull of each region, and computing nearest neighbors. Consequently, a linear array with n processors can solve several image problems in O(n) time which is the same time taken by a two dimensional mesh-connected computer with n2 processors. Limitations of linear arrays are analyzed by presenting a class of image problems which can be solved sequentially in O(n2) time, but require Ω(n2) time on a linear array, irrespective of the number of processors used and the partitioning of the input image among the processors. An alternate communication-efficient fixed-size organization with p processors is proposed to solve such problems in O(n2/p) time, for 1 ≤ p ≤ n.