ZnG：使用新闪存构建GPU多处理器，实现可扩展数据分析
ZnG: Architecting GPU Multi-Processors with New Flash for Scalable Data Analysis
摘要：
我们提出了ZnG，一种新的GPU-SSD集成架构，它可以最大限度地提高GPU中的内存容量，并解决SSD带来的性能损失。具体来说，ZnG将所有GPU内部DRAM替换为超低延迟SSD，以最大限度地提高GPU内存容量。ZnG进一步消除了SSD的性能瓶颈，将其闪存通道替换为高吞吐量闪存网络，并将SSD固件集成到GPU的MMU中，以获得硬件加速的好处。虽然SSD内的闪存阵列可以提供高累积带宽，但由于其访问粒度的不匹配，GPU的存储器请求只能利用这些带宽的一小部分。为了解决这个问题，ZnG采用了一个大型L2缓存和闪存寄存器来缓冲内存请求。我们的评估结果表明，ZnG可以实现比以前的工作高7.5倍的性能。
英文摘要：
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.
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