# ZnG: Architecting GPU Multi-Processors with New Flash for Scalable Data Analysis

arXiv: https://arxiv.org/abs/2006.08975 · HTML: https://mlsanity.com/paper/2006.08975/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2020.

## Built on by (most cited first, 3 of 3)

- [InstInfer: In-Storage Attention Offloading for Cost-Effective Long-Context LLM Inference](https://mlsanity.com/paper/2409.04992/) (2024)
- [G10: Enabling An Efficient Unified GPU Memory and Storage Architecture with Smart Tensor Migrations](https://mlsanity.com/paper/2310.09443/) (2023)
- [Cost-Efficient LLM Training with Lifetime-Aware Tensor Offloading via GPUDirect Storage](https://mlsanity.com/paper/2506.06472/) (2025)

## Abstract (arXiv)

We propose ZnG, a new GPU-SSD integrated architecture, which can maximize the memory capacity in a GPU and address performance penalties imposed by an SSD. Specifically, ZnG replaces all GPU internal DRAMs with an ultra-low-latency SSD to maximize the GPU memory capacity. ZnG further removes performance bottleneck of the SSD by replacing its flash channels with a high-throughput flash network and integrating SSD firmware in the GPU's MMU to reap the benefits of hardware accelerations. Although flash arrays within the SSD can deliver high accumulated bandwidth, only a small fraction of such bandwidth can be utilized by GPU's memory requests due to mismatches of their access granularity. To address this, ZnG employs a large L2 cache and flash registers to buffer the memory requests. Our evaluation results indicate that ZnG can achieve 7.5x higher performance than prior work.