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GPM - Exploring GPUs with Persistent Memory
Series: M.Tech (Research) Colloquium
Speaker: Shweta Pandey
Date/Time: Mar 25 15:00:00
Location: MS Teams
Faculty Advisor: Arkaprava Basu
Non-volatile memory (NVM) technologies promise to blur the long-held distinction between memory and storage by enabling durability at latencies comparable to DRAM at byte granularity.
Persistent Memory (PM) is defined as NVM accessed via load/store instructions at a fine grain.
Due to decade-long research into CPU's software and hardware stack for PM, and with the recent commercialization of NVM under the aegis of Intel Optane, PM's promise of revolutionizing computing seems closer to reality than it has ever been before.
Unfortunately, while a significant portion of computation today happens on Graphics Processing Units (GPUs), they are deprived of leveraging PM. We find that there exist GPU-accelerated applications that could benefit from fine-grain persistence.
Our key goal is to expose byte-grain persistent memory to GPU kernels. For this, we propose a design for GPU with fine-grained access to PM, a.k.a. GPM which combines commercially available GPUs and NVM through software. We find important use-cases to leverage GPM and create a workload suite called GPMBench. GPMBench consists of 11 GPU-accelerated workloads modified to leverage PM. Finally, we demonstrate the benefits of our proposed design, GPM, over conventional methods of persisting from GPU.
Shweta Pandey is an MTech Research student in the Department of Computer Science and Automation at IISC, Bangalore. She is currently advised by Prof. Arkaprava Basu. Her research interests lie in the field of High-Performance Computing. She is currently looking into improving GPU hardware systems for emerging needs.
Host Faculty: Arkaprava Basu