Data Structure Health

Abstract

Applications often have large runtime memory requirements. In some cases, large memory footprint helps accomplish an important functional, performance, or engineering requirement. A large cache, for example, may ameliorate a pernicious performance problem. In general, however, finding a good balance between memory consumption and other requirements is quite challenging. To do so, the development team must distinguish effective from excessive use of memory: when is a data structure too big for its own good?We introduce health signatures to facilitate this balance. Using data from dozens of applications and benchmarks, we show that they provide concise and application-neutral summaries of footprint. We show how to use them to form value judgments about whether a design or implementation choice is good or bad. We demonstrate how to use health signatures to evaluate the asymptotic behavior of these choices, as input data size scales up. Finally, we show how being independent of any application eases comparison across disparate implementations.

The causes of bloat, the limits of health

“…It does not do so in application-neutral terms, nor does it provide predictive models. There are many powerful memory profiling tools [23,25] that identify suspicious data 12 A preliminary version of the work presented in this paper appeared in [17]. types, or memory leaks [11,21,16,4,13].…”