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GPM: Exploring GPUs with Persistent Memory
Series: M.Tech (Research) Thesis Defense
Speaker: Shweta Pandey
Date/Time: Sep 13 11:00:00
Location: Microsoft Teams - Online
Faculty Advisor: Arkaprava Basu
GPUs are a key computing platform for many application domains. While the emergence of non-volatile memory has brought the promise of fine-grain byte-addressable persistence (a.k.a., persistent memory, or PM) to CPU applications, the same unfortunately is beyond the reach of GPU programs.
This work takes three steps toward enabling GPU programs to directly access PM. First, we show how various existing software and hardware technologies can be put together to enable direct access to PM from within a GPU kernel. Next, we created a workload suite of 10 GPU-accelerated applications to demonstrate how GPUs can benefit from PM. We then created a GPU library to support logging, checkpointing, and primitives for native persistence to enable GPU programmers to easily leverage PM.
Shweta Pandey is an MTech (Research) student in the Department of Computer Science and Automation. She is advised by Prof. Arkaprava Basu. She is interested in building systems that use heterogeneous computing and are heterogeneous memory aware.
Host Faculty: Arkaprava Basu