A comparative study of static and profile-based heuristics for inlining

In this paper, we present a comparative study of static and profile-based h euristics for inlining. Our motivation for this study is to use the results to design the best inlining algorithm that we can for the Jalapeno dynamic optimizing compiler for Java [6]. We use a well-known approximation algori thm for the KNAPSACK problem as a common "meta-algorithm" for the inlining heuristics studied in this paper. We present performance results for an imp lementation of these inlining heuristics in the Jalapeno dynamic optimizing compiler. Our performance results show that the inlining heuristics studie d in this paper can lead to significant speedups in execution time (up to 1 .68x) even with modest limits on code size expansion (at most 10%).