Optimising GPGPU Execution Through Runtime Micro-Architecture Parameter Analysis
<|reference_start|>Optimising GPGPU Execution Through Runtime Micro-Architecture Parameter Analysis: 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.<|reference_end|>
@article{sarda2024optimising,
    title={Optimising GPGPU Execution Through Runtime Micro-Architecture Parameter
  Analysis},
    author={Giuseppe M. Sarda and Nimish Shah and Debjyoti Bhattacharjee and Peter
  Debacker and Marian Verhelst},
    journal={2023 IEEE International Symposium on Workload Characterization
  (IISWC)},
    year={2024},
    doi={10.1109/IISWC59245.2023.00017},
    archivePrefix={arXiv},
    eprint={2407.11999},
    primaryClass={cs.AR}
}