Nowadays, query processing in column-store database systems is highly tuned to the underlying (co-)processors. This approach works very well from a performance perspective, but has several shortcomings from a conceptual perspective. For example, this tuning introduces high implementation as well as maintenance cost and one implementation cannot be ported to other (co-)processors. To overcome that, we developed a column-store specific abstraction layer for hardwaredriven vectorization based on the Single Instruction Multiple Data (SIMD) parallel paradigm. Thus, we are able to implement vectorized query operators in a hardware-oblivious manner, which can be specialized to different SIMD instruction set extensions of modern x86-processors. To soften the limitation to x86-processors, we describe our vision to integrate GPUs in our abstraction layer by interpreting GPUs as virtual vector engines in this paper. Moreover, we present some initial evaluation results to determine a reasonable virtual vector size. We conclude the paper with an outlook on our ongoing research in that direction.