Northeastern University

Many developers have begun to realize that heterogeneous multi-core and many-core computer systems can provide sig-nificant performance opportunities to a range of applica-tions. Typical applications possess multiple components that can be parallelized; developers need to be equipped with proper performance tools to analyze program flow and identify ap-plication bottlenecks. In this paper, we analyze and profile the components of the Speeded Up Robust Features (SURF) Computer Vision algorithm written in OpenCL. Our profil-ing framework is developed using built-in OpenCL API func-tion calls, without the need for an external profiler. We show we can begin to identify performance bottlenecks and performance issues present in individual components on dif-ferent hardware platforms. We demonstrate that by using run-time profiling using the OpenCL specification, we can provide an application developer with a fine-grained look at performance, and that this information can be used to tailor performance improvements for specific platforms