Cluster Computing and the Power of Edge Recognition
<|reference_start|>Cluster Computing and the Power of Edge Recognition: We study the robustness--the invariance under definition changes--of the cluster class CL#P [HHKW05]. This class contains each #P function that is computed by a balanced Turing machine whose accepting paths always form a cluster with respect to some length-respecting total order with efficient adjacency checks. The definition of CL#P is heavily influenced by the defining paper's focus on (global) orders. In contrast, we define a cluster class, CLU#P, to capture what seems to us a more natural model of cluster computing. We prove that the naturalness is costless: CL#P = CLU#P. Then we exploit the more natural, flexible features of CLU#P to prove new robustness results for CL#P and to expand what is known about the closure properties of CL#P.   The complexity of recognizing edges--of an ordered collection of computation paths or of a cluster of accepting computation paths--is central to this study. Most particularly, our proofs exploit the power of unique discovery of edges--the ability of nondeterministic functions to, in certain settings, discover on exactly one (in some cases, on at most one) computation path a critical piece of information regarding edges of orderings or clusters.<|reference_end|>
@article{hemaspaandra2005cluster,
    title={Cluster Computing and the Power of Edge Recognition},
    author={Lane A. Hemaspaandra, Christopher M. Homan, Sven Kosub},
    journal={arXiv preprint arXiv:cs/0509060},
    year={2005},
    number={URCS-TR-2005-878},
    archivePrefix={arXiv},
    eprint={cs/0509060},
    primaryClass={cs.CC cs.DM}
}