Profiling and Visualizing Dynamic Pruning Algorithms


Efficiently retrieving the top-$k$ documents for a given query is a fundamental operation in many search applications. Dynamic pruning algorithms accelerate top-$k$ retrieval over inverted indexes by skipping documents that are not able to enter the current set of results. However, the performance of these algorithms depends on a number of variables such as the ranking function, the order of documents within the index, and the number of documents to be retrieved. In this paper, we propose a diagnostic framework, Dyno, for profiling and visualizing the performance of dynamic pruning algorithms. Our framework captures processing traces during retrieval, allowing the operations of the index traversal algorithm to be visualized. These visualizations support both query-level and system-to-system comparisons, enabling performance characteristics to be readily understood for different systems. Dyno benefits both academics and practitioners by furthering our understanding of the behavior of dynamic pruning algorithms, allowing better design choices to be made during experimentation and deployment.

Proceedings of the 46th International ACM Conference on Research and Development in Information Retrieval (SIGIR 2023)