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The search for binary sequences with a high figure of merit, known as the low autocorrelation binary sequence (labs) problem, represents a formidable computational challenge. To mitigate the computational constraints of the problem, we consider solvers that accept odd values of sequence length L and return solutions for skew-symmetric binary sequences only -with the consequence that not all best solutions under this constraint will be optimal for each L. In order to improve both, the search for best merit factor and the asymptotic runtime performance, we instrumented three stochastic solvers, the first two are state-of-the-art solvers that rely on variants of memetic and tabu search (lssMAts and lssRRts), the third solver (lssOrel) organizes the search as a sequence of independent contiguous self-avoiding walk segments. By adapting a rigorous statistical methodology to performance testing of all three combinatorial solvers, experiments show that the solver with the best asymptotic average-case performance, lssOrel 8 = 0.000032 * 1.1504 L , has the best chance of finding solutions that improve, as L increases, figures of merit reported to date. The same methodology can be applied to engineering new labs solvers that may return merit factors even closer to the conjectured asymptotic value of 12.3248.

This paper presents a new method,based on Markov chain analysis, to evaluate the performance of schedules of behavioral specifications. The proposed performance measure is the expected number of clock cycles required by the schedule for a complete execution of the behavioral specification for any distribution of inputs. The measure considers both the repetition of operations (due to loops) and their conditional execution (due to conditional branches). We propose an efficient technique to calculate the metric. We introduce a loop-directed scheduling algorithm (LDS). The algorithm produces schedules such that the expected number of clock cycles, required by the schedule for a complete execution of the behavioral specification, is minimized. Experimental results on several conditional and loop-intensive specifications demonstrate the relevance and effectiveness of both the performance measure and the scheduling algorithm.

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