Персона: Борзунов, Георгий Иванович
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Using statistical analysis to fine-tune the results of knapsack-based computational platform benchmarking
© 2019 IEEE In previous papers, we composed an algorithmic foundation for computational platform benchmarking of well-known exact algorithms for the Knapsack Problem. We suggested using the run time of these algorithms with fixed inputs as the performance estimates. We then derived a single performance estimate, equally impacted by each of the algorithms. Although this approach makes for a reasonable general-purpose benchmark, equalizing the impact of different algorithms is not completely legitimate, as they have different processing requirements. In this paper, we perform an in-depth analysis of algorithm operational requirements and try to fine-tune the integral estimates to describe special-purpose (e.g. data compression or encipherment/decipherment) platforms more accurately.
On using gray codes to improve the efficiency of the parallel exhaustive search algorithm for the knapsack problem
© 2019 IEEE In previous papers, we stated that splitting the lexicographic sequence into equal parts does not yield uniform workload distribution for a parallel computational system. One of the options we have considered is breaking the packing vectors space into classes based on the vectors' Hamming weight, and then split each of the classes into equal parts. In this paper, we investigate another load balancing technique based on using the Gray codes instead of the lexicographic sequence. As only one element of the packing changes on every step, we can calculate the packing weight dynamically. Thus, the time needed to weigh a packing becomes both shorter and more uniform. We study the properties of the Gray codes and analyze their impact on the algorithm run time and efficiency of parallel computation.