论文标题
ZNG：使用新闪光灯架构GPU多处理器，以进行可扩展数据分析
ZnG: Architecting GPU Multi-Processors with New Flash for Scalable Data Analysis
论文作者
论文摘要
我们提出了一种新的GPU-SSD集成体系结构ZNG，它可以最大程度地提高GPU中的内存能力并解决SSD施加的性能惩罚。具体而言，ZNG用超低延迟SSD取代了所有GPU内部DRAM，以最大程度地提高GPU存储能力。 ZNG通过使用高通量闪存网络替换其闪光灯通道并在GPU的MMU中集成SSD固件来进一步消除了SSD的性能瓶颈，从而获得了硬件加速度的好处。尽管SSD内的Flash阵列可以提供高累积的带宽，但由于其访问粒度的不匹配，GPU的内存请求只能使用此类带宽的一小部分。为了解决这个问题，ZNG采用大型L2缓存和Flash寄存器来缓冲内存请求。我们的评估结果表明，ZNG比先前的工作可以取得7.5倍的性能。
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.
