Abstract

In this paper, we present a comparative study of static and dynamic heuristics for inlining. We introduce inlining plans as a formal representation for nested inlining decisions made by an inlining heuristic. We use a well-known approximation algorithm for the knapsack problem as a common "metaalgorithm " for the static and dynamic inlining heuristics studied in this paper. We present performance results for an implementation of these inlining heuristics in the Jalape~no dynamic optimizing compiler for Java. Our performance results show that the inlining heuristics studied in this paper can lead to significant speedups in execution time (up to 1.68\Theta) even with modest limits on code size expansion (at most 10%). # pages excluding title page & bibliography = 15 # pages used by figures and tables = 4 # pages of text = 11 Dynamo '00 submission Page 0 Privileged material --- please do not distribute 1

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