@TechReport{Dartmouth:TR92-183,
  author = {Peter Su},
  title = {{Concurrent Local Search for Fast Proximity Algorithms on Parallel and Vector Architectures}},
  institution = {Dartmouth College, Computer Science},
  address = {Hanover, NH},
  number = {PCS-TR92-183},
  year = {1992},
  URL = {http://www.cs.dartmouth.edu/reports/TR92-183.pdf},
  abstract = {
    	This paper presents a fast algorithm for solving the
    all-nearest-neighbors problem. The algorithm uses a data parallel
    style of programming which can be efficiently utilized on a variety of
    parallel and vector architectures [4,21,26]. I have implemented the
    algorithm in C on one such architecture, the Cray Y-MP.  On one Cray
    CPU, the implementation is about 19 times faster than a fast
    sequential algorithm running on a Sparc workstation.
    	The main idea in the algorithm is to divide the plane up into
    a fixed grid of cells, or buckets.  When the points are well
    distributed, the algorithm processes each query point, q, by searching
    a small number of cells close to q.  Bentley, WEide and Yao first
    presented this idea for conventional architectures [3], but the
    technique works equally well on parallel and vector machines, leading
    to a simple, efficient algorithm.
    	We can also use the cell technique to solve a wide variety of basic computational problems such as finding 
    closest pairs, sorting and constructing Voronoi diagrams and Delaunay triangulations.
  }
}