@article{klashtorny_23_pws,
  author = {Klashtorny, Artem and Wu, Zhuanhao and Kaushik, Anirudh Mohan and Patel, Hiren},
  title = {Predictable GPU Wavefront Splitting for Safety-Critical Systems},
  year = {2023},
  abbr = {CASES},
  issue_date = {October 2023},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  volume = {22},
  number = {5s},
  issn = {1539-9087},
  url = {https://doi.org/10.1145/3609102},
  doi = {10.1145/3609102},
  abstract = {We present a predictable wavefront splitting (PWS) technique for graphics processing units (GPUs). PWS improves the performance of GPU applications by reducing the impact of branch divergence while ensuring that worst-case execution time (WCET) estimates can be computed. This makes PWS an appropriate technique to use in safety-critical applications, such as autonomous driving systems, avionics, and space, that require strict temporal guarantees. In developing PWS on an AMD-based GPU, we propose microarchitectural enhancements to the GPU, and a compiler pass that eliminates branch serializations to reduce the WCET of a wavefront. Our analysis of PWS exhibits a performance improvement of 11\% over existing architectures with a lower WCET than prior works in wavefront splitting.},
  journal = {ACM Trans. Embed. Comput. Syst.},
  month = sep,
  articleno = {107},
  numpages = {25},
  keywords = {safety-critical systems, GPU},
  pdf = {yes},
  public = {yes},
  month_numeric = {9}
}