A Framework for Performance-Aware Composition of Applications for GPU-Based Systems

User-level components of applications can be made performance-aware by annotating them with performance model and other metadata. We present a component model and a composition framework for the performance-aware composition of applications for modern GPU-based systems from such components, which may expose multiple implementation variants. The framework targets the composition problem in an integrated manner, with particular focus on global performance-aware composition across multiple invocations. We demonstrate several key features of our framework relating to performance-aware composition including implementation selection, both with performance characteristics being known (or learned) beforehand as well as cases when they are learned at runtime. We also demonstrate hybrid execution capabilities of our framework on real applications. Furthermore, as an important step towards global composition, we present a bulk composition technique that can make better composition decisions by considering information about upcoming calls along with data flow information extracted from the source program by static analysis, thus improving over the traditional greedy performance-aware policy that only considers the current call for optimization.