Progressive Profiling: A Methodology Based on Profile Propagation and Selective Profile Collection

In recent years, Profile-Based Optimization (PBO) has become a key technique in program optimization. In PBO, the optimizer uses information gathered during previous program executions to guide the optimization process. Even though PBO has been implemented in many research systems and some software companies, there has been little research on how to make PBO effective in practice. In today’s software industry, one major hurdle in applying PBO is the conflict between the need for high-quality profiles and the lack of time for long profiling runs. For PBO to be effective, the profile needs to be representative of how the users or a particular user runs the program. For many modern applications that are large and interactive, it takes a significant amount of time to collect high-quality profiles. This problem will only become more prominent as application programs grow more complex. A lengthy profiling process is especially impractical in software production environments, where programs are modified and rebuilt almost daily. Without enough time for extensive profiling runs, the benefit from applying PBO is severely limited. This in turn hampers the interest in running PBO and increases the dependency on hand tuning in software development and testing. In order to obtain high-quality profiles in a software production environment without lengthening the daily build cycle, we seek to change the current practice where a new profile must be generated from scratch for each new program version. Most of today’s