Currently, the most energy-efficient hardware platforms for floating point-intensive calculations (also known as High Performance Computing, or HPC) are graphical processing units (GPUs). However, porting existing scientific codes to GPUs can be far from trivial. This article summarizes our recent advances in enabling machine-assisted, HPC-oriented refactorings with reference to existing APIs and programming idioms available in C and C++. The tool we are extending and using for the purpose is called Coccinelle. An important workflow we aim to support is that of writing and maintaining tersely written application code, while deferring circumstantial, ad-hoc, performance-related changes to specific, separate rules called semantic patches. GPUs currently offer very limited debugging facilities. The approach we are developing aims at preserving intelligibility, longevity, and relatedly, debuggability of existing code on CPUs, while at the same time enabling HPC-oriented code evolutions such as introducing support for GPUs, in a scriptable and possibly parametric manner. This article sketches a number of self-contained use cases, including further HPC-oriented cases which are independent from GPUs.