Parametric Prediction of Heap Memory Requirements

V. BRABERMAN; F. FERNÁNDEZ; D. GARBERVETSKY; S. YOVINE

Tucson, EEUU

Conferencia; ACM SIGPLAN International Symposium on Memory Management; 2008

ACM SigPlan

This work presents a technique to compute symbolic polynomial approximations of the amount of dynamic memory required to safely execute a method without running out of memory, for Javalike imperative programs. More precisely, given an initial configuration of the stack and the heap, the peak memory consumption is the maximum space occupied by newly created objects in all states along a run from it. We over-approximate the peak memory consumption using a scoped-memory management where objects are organized in regions associated with the lifetime of methods. We model the problem of computing the maximum memory occupied by any region configuration as a parametric polynomial optimization problem over a polyhedral domain and resort to Bernstein basis to solve it. We apply the developed tool to several benchmarks