We define a novel approach for optimizing the use of libraries within applications. We propose that library-defined abstractions
be annotated with additional semantics to support their automated optimization. By leveraging these additional semantics we
enable specialized optimizations of application codes which use library abstractions. We believe that such an approach entails
the use of formal methods.
It is a common perception that performance is inversely proportional to the level of abstraction. Our work shows that this
is not the case if the additional semantics of library-defined abstractions can be leveraged. We describe ROSE, a framework
for building source-to-source translators that perform high-level optimizations on scientific applications. ROSE allows the
recognition of library abstractions and the optimization of their use in applications. We show how ROSE can utilize the semantics
of userdefined abstractions in libraries within the compile-time optimization of applications.