Griffin: Hardware-Software Support for Efficient Page Migration in Multi-GPU Systems

Baruah T., Sun Y., Dincer A. T., Mojumder S. A., Abellan J. L., Ukidave Y., ...More

26th IEEE International Symposium on High Performance Computer Architecture (HPCA), California, United States Of America, 22 - 26 February 2020, pp.596-609 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • Doi Number: 10.1109/hpca47549.2020.00055
  • City: California
  • Country: United States Of America
  • Page Numbers: pp.596-609
  • Istanbul Technical University Affiliated: Yes


As transistor scaling becomes increasingly more difficult to achieve, scaling the core count on a single GPU chip has also become extremely challenging. As the volume of data to process in today's increasingly parallel workloads continues to grow unbounded, we need to find scalable solutions that can keep up with this increasing demand. To meet the need of modern-day parallel applications, multi-GPU systems offer a promising path to deliver high performance and large memory capacity. However, multi-GPU systems suffer from performance issues associated with GPU-to-GPU communication and data sharing, which severely impact the benefits of multi-GPU systems. Programming multi-GPU systems has been made considerably simpler with the advent of Unified Memory which enables runtime migration of pages to the GPU on demand.