GPM: Leveraging Persistent Memory from a GPU

Shweta Pandey*, Aditya K Kamath*, and Arkaprava Basu
(*Authors contributed equally to this work)

Published in ACM 27th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), 2022

The GPU is a key computing platform for many application domains. While new non-volatile memory technology has brought the promise of byte-addressable persistence (a.k.a., persistent memory, or PM) to CPU applications, the same, unfortunately, is beyond the reach of GPU programs.

We take three key steps toward enabling GPU programs to access PM directly. First, enable direct access to PM from within a GPU kernel without needing to modify the hardware. Next, we demonstrate three classes of GPU-accelerated applications that benefit from PM. In the process, we create a workload suite with nine such applications. We then create a GPU library, written in CUDA, to support logging, checkpointing, and primitives for native persistence for programmers to easily leverage PM.