The causes of bloat, the limits of 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. We introduce health signatures to enable these distinctions. 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 show how being independent of any application eases comparison across disparate implementations. We demonstrate the asymptotic nature of memory health: certain designs are limited in the health they can achieve, no matter how much the data size scales up. Finally, we show how to use health signatures to automatically generate formulas that predict this asymptotic behavior, and show how they enable powerful limit studies on memory health. Copyright © 2007 ACM.