Processing large numbers of key/value lookups is an integral part of modern server databases and other "Big Data" applications. Prior work has shown that hash table based key/value lookups can benefit significantly from using a dedicated hardware lookup accelerator placed near memory. However, previous evaluations of this design on the Logic in Memory Emulator (LiME) were limited by the capabilities of the hardware on which it was emulated, which only supports a single CPU core and a single near-memory lookup engine. We extend the emulation results by incorporating simulation to evaluate this design in additional scenarios. By incorporating an HMC simulation model, we design optimizations that better mitigate the effects of the HMC closed page policy and that better utilize the HMC's parallelism, improving predicted performance by an order of magnitude. Additionally, we use simulation to evaluate the scaling performance of multiple near-memory lookup accelerators. Our work employs an open source emulator LiME, open source simulatation infrastructure SST, and the open source HMC-Sim simulator.
翻译:处理大量关键/价值查看是现代服务器数据库和其他“ 大数据” 应用程序的一个组成部分。 先前的工作显示, 以散列表格为基础的关键/ 价值查看能够极大地受益于使用贴近记忆的专用硬件查看加速器。 然而, 先前在记忆模拟器( LiME) 上的这一设计评价受到所模仿的硬件能力的限制, 该功能仅支持一个单个 CPU 核心和一个接近模版的搜索引擎。 我们通过模拟来扩展模拟结果, 以便在其他情况下评估这一设计。 我们设计了一种HMC 模拟模型, 以更好地减轻 HMC 封闭页面政策的效果, 并更好地利用 HMC 平行政策, 以数量顺序改进预测的性能。 此外, 我们使用模拟来评估多个近模调搜索加速器的缩放性能。 我们的工作使用了一种开源模拟器 LIME、 开源模拟基础设施 SST 和 开源 HMC 模拟器 。