论文标题
Cuttlesys：可重新配置多功能的数据驱动的资源管理
CuttleSys: Data-Driven Resource Management forInteractive Applications on Reconfigurable Multicores
论文作者
论文摘要
关键潜伏应用的多租期导致重新源干扰和不可预测的性能。核心重新配置为托管提供了更多的机会，因为它允许硬件适应共同安排应用程序的特定组合的动态性能和功率需求。但是，重新配置性也引入了挑战，即使对于少数可重新配置的内核，探索设计空间也会变得更加时间和资源。 我们提出了Cuttlesys，这是可重构多核的运行时，利用可扩展和轻量级数据挖掘的运行时间，以快速识别一组共同安排应用程序的合适的核心和高速缓存配置。运行时结合了协作过滤，以推断每个核心和缓存配置上每个作业的行为，并具有动态尺寸搜索以有效地探索配置空间。我们评估了具有数十个可重构核心的多具的Cuttlesys，在严格的功率约束下，与核心级别的门控和类似甲骨文的不对称的多门相比，分别与核心级别的门控和类似甲骨文的不对称多门相比，最多显示2.46倍和1.55倍的性能。
Multi-tenancy for latency-critical applications leads to re-source interference and unpredictable performance. Core reconfiguration opens up more opportunities for colocation,as it allows the hardware to adjust to the dynamic performance and power needs of a specific mix of co-scheduled applications. However, reconfigurability also introduces challenges, as even for a small number of reconfigurable cores, exploring the design space becomes more time- and resource-demanding. We present CuttleSys, a runtime for reconfigurable multi-cores that leverages scalable and lightweight data mining to quickly identify suitable core and cache configurations for a set of co-scheduled applications. The runtime combines collaborative filtering to infer the behavior of each job on every core and cache configuration, with Dynamically Dimensioned Search to efficiently explore the configuration space. We evaluate CuttleSys on multicores with tens of reconfigurable cores and show up to 2.46x and 1.55x performance improvements compared to core-level gating and oracle-like asymmetric multicores respectively, under stringent power constraints.
