NC理论的经验挑战(摘要)

Ananth Hari, U. Vishkin
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引用次数: 0

摘要

Horn-satisfiability或Horn-sat是确定Horn公式是否存在令人满意的赋值的问题,Horn公式是一个子句的连接,每个子句最多有一个正字面量(也称为Horn子句)。这是一个众所周知的P完全问题,这意味着除非P = NC,否则它是一个很难并行化的问题。在本文中,我们通过经验证明,在一个已知的用于生成Horn公式的简单随机模型下,难以并行化的实例(更接近最坏情况的行为)的比例是无穷小的。我们表明,对于几乎所有实例,Horn-SAT并行算法的深度平均是多对数的,同时保持工作线性。这对理论家和程序员提出了挑战,要求他们超越最坏情况分析,提出具有各自性能保证的实用算法。
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Empirical Challenge for NC Theory (Abstract)
Horn-satisfiability or Horn-SAT is the problem of deciding whether a satisfying assignment exists for a Horn formula, a conjunction of clauses each with at most one positive literal (also known as Horn clauses). It is a well-known P-complete problem, which implies that unless P = NC, it is a hard problem to parallelize. In this paper, we empirically show that, under a known simple random model for generating the Horn formula, the ratio of hard-to-parallelize instances (closer to the worst-case behavior) is infinitesimally small. We show that the depth of a parallel algorithm for Horn-SAT is polylogarithmic on average, for almost all instances, while keeping the work linear. This challenges theoreticians and programmers to look beyond worst-case analysis and come up with practical algorithms coupled with respective performance guarantees.
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