Lune

ASPLOS2023Top-tier venue

Scoped Buffered Persistency Model for GPUs

Shweta Pandey, Aditya K. Kamath, Arkaprava Basu

2023Year
6Citations
3Top-tier citations

Abstract

While the implications of persistent memory (PM) on CPU hardware and software are well-explored, the same is not true for GPUs (Graphics Processing Units). A recent work, GPM, demonstrated how GPU programs can benefit from the fine-grain persistence of PM. However, in the absence of a persistency model, one cannot reason about the correctness of PM-aware GPU programs. Persistency models define the order in which writes to PM are persisted. We explore persistency models for GPUs.

We explore persistency models for GPUs. We demonstrate that CPU persistency models fall short for GPUs. We qualitatively and quantitatively argue that GPU persistency models should support scopes and buffering of writes to PM to leverage parallelism while adapting to higher NVM latencies. We formally specify a GPU persistency model that supports both scopes and buffers. We detail how GPU architecture can efficiently realize such a model. Finally, we quantitatively demonstrate the usefulness of scopes and buffers for PM-aware GPU programs.

Ask about this paper

Your agent reads all of it.

Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.

Questions to start from

Your agent calls

Luneget_paper_fulltext

Ask in Lune

Free to start. No credit card required.

lune papers fulltext caad086d-294b-4515-aee3-dd55f6c6e67f

Cited by top-tier papers3

Ask how each one uses it

Builds on15

Related papers

Dusk over the sea between two cliffs drawn in fine vertical lines