Dynamic GMMU Bypass for Address Translation in Multi-GPU Systems

EasyChair Preprint no. 4179

Abstract

The ever increasing application footprint raises challenges for GPUs. As Moore’s Law reaches its limit, it is not easy to improve single GPU performance any further; instead, multi-GPU systems have been shown to be a promising solution due to its GPU-level parallelism. Besides, memory virtualization in recent GPUs simplifies multi-GPU programming. Memory virtualization requires support for address translation, and the overhead of address translation has an important impact on the system’s performance. Currently, there are two common address translation architectures in multi-GPU systems, including distributed and centralized address translation architectures. We find that both architectures suffer from performance loss in certain cases. To address this issue, we propose GMMU Bypass, a technique that allows address translation requests to dynamically bypass GMMU in order to reduce translation overhead. Simulation results show that our technique outperforms distributed address translation architecture by 6% and centralized address translation architecture by 106% on average.

Keyphrases: Address translation architecture, memory virtualization, Multi-GPU system

@Booklet{EasyChair:4179,
  author = {Jinhui Wei and Jianzhuang Lu and Qi Yu and Chen Li and Yunping Zhao},
  title = {Dynamic GMMU Bypass for Address Translation in Multi-GPU Systems},
  howpublished = {EasyChair Preprint no. 4179},

  year = {EasyChair, 2020}}