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abstract

A High-bandwidth High-capacity Hybrid 3D Memory for GPUs

Published: 10 June 2024 Publication History

Abstract

GPUs execute thousands of active threads simultaneously, requiring high memory bandwidth to handle multiple memory requests efficiently. The memory bandwidth in GPUs has always been increasing, but it is still insufficient for the demands of fine-grained threads, necessitating a higher memory bandwidth. Important workloads like deep learning and data analytics demand terabytes of data processing, necessitating high memory capacity and bandwidth to avoid performance overheads. True-3D stacking of non-volatile memory layers on GPUs can provide the required higher bandwidth and capacity, enhancing performance and energy efficiency. We propose a high-bandwidth high-capacity hybrid 3D memory (H3DM) that doubles bandwidth through true-3D integration compared to the baseline GPU architecture and affords 272 GB of total memory capacity by stacking 8 PCM layers (each of 32 GB) and two DRAM layers (each of 8 GB).

References

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Esha Choukse, Michael B Sullivan, Mike O'Connor, Mattan Erez, Jeff Pool, David Nellans, and Stephen W Keckler. 2020. Buddy compression: Enabling larger memory for deep learning and HPC workloads on gpus. In 2020 ACM/IEEE 47th Annual International Symposium on Computer Architecture (ISCA). IEEE, 926--939.
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Debashis Ganguly, Ziyu Zhang, Jun Yang, and Rami Melhem. 2020. Adaptive page migration for irregular data-intensive applications under gpu memory oversubscription. In 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS). IEEE, 451--461.
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Soheil Khadirsharbiyani, Jagadish Kotra, Karthik Rao, and Mahmut Kandemir. 2022. Data Convection: A GPU-Driven Case Study for Thermal-Aware Data Placement in 3D DRAMs. In SIGMETRICS/PERFORMANCE '22: ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems, Mumbai, India, June 6 - 10, 2022. ACM, 37--38.
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Dongki Kim, Sungkwang Lee, Jaewoong Chung, Dae Hyun Kim, Dong Hyuk Woo, Sungjoo Yoo, and Sunggu Lee. 2012. Hybrid DRAM/PRAM-based main memory for single-chip CPU/GPU. In DAC Design Automation Conference 2012. IEEE, 888--896.
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Maohua Zhu, Youwei Zhuo, Chao Wang, Wenguang Chen, and Yuan Xie. 2018. Performance evaluation and optimization of HBM-Enabled GPU for data-intensive applications. IEEE Transactions on Very Large Scale Integration (VLSI) Systems 26, 5 (2018), 831--840.

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cover image ACM Conferences
SIGMETRICS/PERFORMANCE '24: Abstracts of the 2024 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems
June 2024
120 pages
ISBN:9798400706240
DOI:10.1145/3652963
  • cover image ACM SIGMETRICS Performance Evaluation Review
    ACM SIGMETRICS Performance Evaluation Review  Volume 52, Issue 1
    SIGMETRICS '24
    June 2024
    104 pages
    DOI:10.1145/3673660
    • Editor:
    • Bo Ji
    Issue’s Table of Contents
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 10 June 2024

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Author Tags

  1. capacity
  2. gpus
  3. memory system
  4. pcm
  5. power
  6. true 3d

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