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

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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 7.5× higher performance than prior work.
Publisher
Institute of Electrical and Electronics Engineers Inc.
Issue Date
2020-06-03
Language
English
Citation

47th ACM/IEEE Annual International Symposium on Computer Architecture, ISCA 2020, pp.1064 - 1075

DOI
10.1109/ISCA45697.2020.00090
URI
http://hdl.handle.net/10203/278539
Appears in Collection
EE-Conference Papers(학술회의논문)
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