In-Situ Profiling Feedback for GPGPU Code

The evolving landscape of software development increasingly prioritizes functionality, maintainability, and developer productivity. This typically comes hand in hand with the shortcoming that less focus is invested on optimizing for runtime performance of programs. However, optimizing for performance is an important task in time-critical domains. Additionally, optimizing for performance can be an important way of reducing actual hardware requirements and achieving a better ecological footprint. So, why not bringing program optimization closer to the software engineer and reducing the disconnect between profiling results and their interpretability? This poster presents a GPU-focused in-situ profiling approach that visualizes memory profiling metrics directly inside the source code and gives the software engineer an direct hint for identifying inefficient parts during development. Performance metrics evaluated on each line are highlighted in the source code.