This paper addresses the gap between envisioned hardware-virtualized techniques for GPU programming and a conventional approach from the point of view of an application engineer taking software engineering aspects like maintainability, understandability and productivity, and resulting achieved gain in performance and scalability into account. This gap is discussed on the basis of use cases from the field of image processing, and illustrated by means of performance benchmarks as well as evaluations regarding software engineering productivity.