Introduction to algorithms
Static Rate-Optimal Scheduling of Iterative Data-Flow Programs Via Optimum Unfolding
IEEE Transactions on Computers
Bounded incremental computation
Bounded incremental computation
LEDA: a platform for combinatorial and geometric computing
Communications of the ACM
Determining the minimum iteration period of an algorithm
Journal of VLSI Signal Processing Systems
An incremental algorithm for a generalization of the shortest-path problem
Journal of Algorithms
Efficient algorithms for optimum cycle mean and optimum cost to time ratio problems
Proceedings of the 36th annual ACM/IEEE Design Automation Conference
Incremental evaluation of computational circuits
SODA '90 Proceedings of the first annual ACM-SIAM symposium on Discrete algorithms
Scheduling Parallel Computations
Journal of the ACM (JACM)
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Evolutionary computation: comments on the history and current state
IEEE Transactions on Evolutionary Computation
Instruction level parallelism of non-uniform acyclic loops
Journal of Computing Sciences in Colleges
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The problem of detecting negative weight cycles in a graph is examined in the context of the dynamic graph structures that arise in the process of high level synthesis (HLS). The concept of adaptive negative cycle detection is introduced, in which a graph changes over time and negative cycle detection needs to be done periodically, but not necessarily after every individual change. We present an algorithm for this problem, based on a novel extension of the well-known Bellman-Ford algorithm that allows us to adapt existing cycle information to the modified graph, and show by experiments that our algorithm significantly outperforms previous incremental approaches for dynamic graphs. In terms of applications, the adaptive technique leads to a very fast implementation of Lawlers algorithm for the computation of the maximum cycle mean (MCM) of a graph, especially for a certain form of sparse graph. Such sparseness often occurs in practical circuits and systems, as demonstrated, for example, by the ISCAS 89/93 benchmarks. The application of the adaptive technique to design-space exploration (synthesis) is also demonstrated by developing automated search techniques for scheduling iterative data-flow graphs.