SEGREGATING HEAP OBJECTS BY REFERENCE BEHAVIOR AND LIFETIME

Dynamic storage allocation has become increasingly important in many a pplications, in part due to the use of the object-oriented paradigm. A t the same time, processor speeds are increasing faster than memory sp eeds and programs are increasing in size faster than memories. In this paper, we investigate efforts to predict heap object reference and li fetime behavior at the time objects are allocated. Our approach uses p rofile-based optimization, and considers a variety of different inform ation sources present at the time of object allocation to predict the object's reference frequency and Lifetime. Our results, based on measu rements of six allocation intensive programs, show that program refere nces to heap objects are highly predictable and that our prediction me thods can successfully predict the behavior of these heap objects. We show that our methods can decrease the page fault rate of the programs measured, sometimes dramatically, in cases where the physical memory available to the program is constrained.