Optimal Circular Arc Representations: Properties, Recognition, and Construction

AbstractWe investigate some properties of minimal interval and circular arc representations and give several optimal sequential and parallel recognition and construction algorithms. We show that, among other things, given ans×tinterval or circular arc representation matrix, •deciding if the representation is minimal can be done inO(logs) time withO(st/logs) EREW PRAM processors, or inO(1) time withO(st) common CRCW PRAM processors; •constructing an equivalent minimum interval representation can be done inO(log(st)) time withO(st/log(st)) EREW PRAM processors, or inO(logt/loglogt) time withO(stloglogt/logt) common CRCW PRAM processors, or inO(1) time withO(st) BSR processors; •constructing an equivalent minimal circular arc representation can be done inO(st) time

A decoupled local memory allocator

Compilers use software-controlled local memories to provide fast, predictable, and power-efficient access to critical data. We show that the local memory allocation for straight-line, or linearized programs is equivalent to a weighted interval-graph coloring problem. This problem is new when allowing a color interval to "wrap around," and we call it the submarine-building problem. This graph-theoretical decision problem differs slightly from the classical ship-building problem, and exhibits very interesting and unusual complexity properties. We demonstrate that the submarine-building problem is NP-complete, while it is solvable in linear time for not-so-proper interval graphs, an extension of the the class of proper interval graphs. We propose a clustering heuristic to approximate any interval graph into a not-so-proper interval graph, decoupling spill code generation from local memory assignment. We apply this heuristic to a large number of randomly generated interval graphs reproducing the statistical features of standard local memory allocation benchmarks, comparing with state-of-the-art heuristics. © 2013 ACM