Benchmarking purely functional data structures

When someone designs a new data structure, they want to know how well it performs. Previously, the only way to do this involves nding, coding and testing some applications to act as benchmarks. This can be tedious and time-consuming. Worse, how a benchmark uses a data structure may considerably a ect the e ciency of the data structure. Thus, the choice of benchmarks may bias the results. For these reasons, new data structures developed for functional languages often pay little attention to empirical performance. We solve these problems by developing a benchmarking tool, Auburn, that can generate benchmarks across a fair distribution of uses. We precisely de ne \the use of a data structure", upon which we build the core algorithms of Auburn: how to generate a benchmark from a description of use, and how to extract a description of use from an application. We consider how best to use these algorithms to benchmark competing data structures. Finally, we test Auburn by benchmarking several implementations of three common data structures: queues, random-access lists, and heaps. These and other results show Auburn to be a useful and accurate tool. They also reveal areas requiring improvement, which we list as future work. ii