  An inherently parallel algorithm is proposed that efficiently performs
selection: finding the K-th largest member of a set of N members. Selection is
a common component of many more complex algorithms and therefore is a widely
studied problem.
  Not much is new in the proposed dualheap selection algorithm: the heap data
structure is from J.W.J.Williams, the bottom-up heap construction is from R.W.
Floyd, and the concept of a two heap data structure is from J.W.J. Williams and
D.E. Knuth. The algorithm's novelty is limited to a few relatively minor
implementation twists: 1) the two heaps are oriented with their roots at the
partition values rather than at the minimum and maximum values, 2)the coding of
one of the heaps (the heap of smaller values) employs negative indexing, and 3)
the exchange phase of the algorithm is similar to a bottom-up heap
construction, but navigates the heap with a post-order tree traversal.
  When run on a single processor, the dualheap selection algorithm's
performance is competitive with quickselect with median estimation, a common
variant of C.A.R. Hoare's quicksort algorithm. When run on parallel processors,
the dualheap selection algorithm is superior due to its subtasks that are
easily partitioned and innately balanced.
