Systems that comprise accelerators (e.g., GPUs) promise high performance, but their programming is still a challenge, mainly because of two reasons: 1) two distinct programming models have to be used within an application: one for the host CPU (e.g., C++), and one for the accelerator (e.g., OpenCL or CUDA); 2) using Just-In-Time (JIT) compilation and its optimization opportunities in both OpenCL and CUDA requires a cumbersome preparation of the source code. These two aspects currently lead to long, poorly structured, and error-prone GPU codes. Our PACXX programming approach addresses both aspects: 1) parallel programs are written using exclusively the C++ programming language, with modern C++14 features including variadic templates, generic lambda expressions, as well as STL containers and algorithms; 2) a simple yet powerful API (PACXX-Reflect) is offered for enabling JIT in GPU kernels; it uses lightweight runtime reflection to modify the kernel's behaviour during runtime. We show that PACXX codes using the PACXX-Reflect are about 60% shorter than their OpenCL and CUDA Toolkit equivalents and outperform them by 5% on average.