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Identifying the hottest paths in the control flow graph of a routine can direct optimizations to portions of the code where most resources are consumed. This powerful methodology, called path profiling, was introduced by Ball and Larus in the mid 90's [4] and has received considerable attention in the last 15 years for its practical relevance. A shortcoming of the Ball-Larus technique was the inability to profile cyclic paths, making it difficult to mine execution patterns that span multiple loop iterations. Previous results, based on rather complex algorithms, have attempted to circumvent this limitation at the price of significant performance losses even for a small number of iterations. In this paper, we present a new approach to multi-iteration path profiling, based on data structures built on top of the original Ball-Larus numbering technique. Our approach allows the profiling of all executed paths obtained as a concatenation of up to k Ball-Larus acyclic paths, where k is a user-defined parameter. We provide examples showing that this method can reveal optimization opportunities that acyclic-path profiling would miss. An extensive experimental investigation on a large variety of Java benchmarks on the Jikes RVM shows that our approach can be even faster than Ball-Larus due to fewer operations on smaller hash tables, producing compact representations of cyclic paths even for large values of k.

Ball-Larus path profiling across multiple loop iterations