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CHAMELEON: A Dynamically Reconfigurable Heterogeneous Memory System

机译:变色龙:动态可重构的异构内存系统

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Modern computing systems and applications have growing demand for memories with higher bandwidth. This demand can be alleviated using fast, large on-die or die-stacked memories. They are typically used with traditional DRAM as part of a heterogeneous memory system and used either as a DRAM cache or as a hardware-or OS-managed part of memory (PoM). Caches adapt rapidly to application needs and typically provide higher performance but reduce the total OS-visible memory capacity. PoM architectures increase the total OS-visible memory capacity but exhibit additional overheads due to swapping large blocks of data between fast and slow memory. In this paper, we propose Chameleon, a hybrid architecture that bridges the gap between cache and PoM architectures. When applications need a large memory, Chameleon uses both fast and slow memories as PoM, maximizing the available space for the application. When the application's footprint is smaller than the total physical memory capacity, Chameleon opportunistically uses free space in the system as a hardware-managed cache. Chameleon is a hardware-software co-designed system where the OS notifies the hardware of pages that are allocated or freed, and hardware decides on switching memory regions between PoM-and cache-modes dynamically. Based on our evaluation of multi-programmed workloads on a system with 4GB fast memory and 20GB slow memory, Chameleon improves the average performance by 11.6% over PoM and 24.2% over a latency-optimized cache.
机译:现代化的计算系统和应用程序对具有更高带宽的回忆需求不断增长。这种需求可以使用快速,大的模具或模具堆叠的存储器来缓解。它们通常与传统的DRAM一起使用,作为异构内存系统的一部分,并且用作DRAM缓存或作为内存(POM)的硬件或OWS管理部分。高速缓存迅速适应应用需求,通常提供更高的性能,但降低了总操作的内存容量。 POM架构增加了总操作系统可见存储器容量,但由于在快速和慢速存储器之间交换大块数据块,展示了额外的开销。在本文中,我们提出了一个混合座,一个混合架构,它弥合了缓存和POM架构之间的差距。当应用程序需要大存储器时,变色龙将使用快速和慢回忆作为POM,最大化应用程序的可用空间。当应用程序的占用空间小于整体物理内存容量时,Chameleon机会使用系统中的可用空间作为硬件管理的缓存。 Chameleon是一个硬件 - 软件共同设计的系统,其中OS通知分配或释放的页面的硬件,硬件决定动态地切换POM和高速缓存模式之间的内存区域。基于我们对具有4GB快速内存和20GB缓慢存储器的系统对系统的多程序工作负载的评估,Chameleon通过POM提高了11.6%的平均性能,并在延迟优化的缓存中提高了24.2%。

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