Efficient object sampling via weak references

The performance of automatic memory management may be improved if the polic ies used in allocating and collecting objects had knowledge of the lifetime s of objects. To date, approaches to the pretenuring of objects in older ge nerations have relied on profile-driven feedback gathered from trace runs. This feedback has been used to specialize allocation sites in a program. Th ese approaches suffer from a number of limitations. We propose an alternati ve that through efficient sampling of objects allows for on-line adaption o f allocation sites to improve the efficiency of the memory system. In doing so, we make use of a facility already present in many collectors such as t hose found in Java(TM) virtual machines: weak references. By judiciously tr acking a subset of allocated objects with weak references, we are able to g ather the necessary statistics to make better object-placement decisions.