  GPGPU execution analysis has always been tied to closed-source, proprietary
benchmarking tools that provide high-level, non-exhaustive, and/or statistical
information, preventing a thorough understanding of bottlenecks and
optimization possibilities. Open-source hardware platforms offer opportunities
to overcome such limits and co-optimize the full {hardware-mapping-algorithm}
compute stack. Yet, so far, this has remained under-explored. In this work, we
exploit micro-architecture parameter analysis to develop a hardware-aware,
runtime mapping technique for OpenCL kernels on the open Vortex RISC-V GPGPU.
Our method is based on trace observations and targets optimal hardware resource
utilization to achieve superior performance and flexibility compared to
hardware-agnostic mapping approaches. The technique was validated on different
architectural GPU configurations across several OpenCL kernels. Overall, our
approach significantly enhances the performance of the open-source Vortex
GPGPU, contributing to unlocking its potential and usability.
