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ASPLOS2022顶会

GPM: leveraging persistent memory from a GPU

Shweta Pandey, Aditya K. Kamath, Arkaprava Basu

2022年份
18被引次数
7顶会引用

摘要

The GPU is a key computing platform for many application domains. While the 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.

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