Juggling Versus Three-Way-Reversal Sequence Rotation Performance Across Four Data Types
Keywords:
juggling rotation, sequence rotation, sequence reversal, array rotation, rotation algorithmAbstract
Systems analysts, designers, and programmers normally choose among candidate algorithms to develop applications. This choice is even more crucial when the application is life critical, memory and/or time bound, such as embedded real-time systems. One of such choice making is in two leading candidate, high performance, sequence rotation algorithms ‒ juggling versus three-way-reversal ‒ often required for in-place sorting. The problem is identifying the best performing one among them and whether changes of elements versus indexes weights, in the form of altered data types, would shifts the performance index. This paper is therefore focused on investigating the performances differences of these algorithms across data types in adequately long stretched sequence, using both theoretical and statistical analysis. The objective is to measure the efficiencies of the algorithms around the variability of elements versus indexes weights. Using Java to implement the algorithms, pool of 35,500 records of execution timing for each of the data types were generated. Out of this number, 355 randomly sampled records from every next 100 were collected. Two-way analysis of variance (ANOVA) was carried out using R statistical package. The results of the analysis indicated that theoretically, juggling rotation might perform better in terms of time utilization. But statistically, the hypothesis test strongly suggested the contrary. The implication is that, with this study, users are better informed of which algorithm to adopt in practice. Given the conflicting theoretical and statistical results, it becomes necessary to investigate further the experimental low performance of juggling rotation.
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