Optimizing Program Performance via Similarity, Using a Feature-Agnostic Approach

This work proposes a new technique for performance evaluation to predict performance of parallel programs across diverse and complex systems. In this work the term system is comprehensive of the hardware organization, the development and execution environment. The proposed technique considers the collection of completion times for some pairs (program, system) and constructs an empirical model that learns to predict performance of unknown pairs (program, system). This approach is feature-agnostic because it does not involve previous knowledge of program and/or system characteristics (features) to predict performance. Experimental results conducted with a large number of serial and parallel benchmark suites, including SPEC CPU2006, SPEC OMP2012, and systems show that the proposed technique is equally applicable to be employed in several compelling performance evaluation studies, including characterization, comparison and tuning of hardware configurations, compilers, run-time environments or any combination thereof.