A Case for Granularity Aware Page Migration
A Case for Granularity Aware Page Migration
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DOI：
10.1145/3205289.3208064
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发表时间：
2018-06
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影响因子：
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通讯作者：
Jee Ho Ryoo;L. John;Arkaprava Basu
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作者：
Jee Ho Ryoo;L. John;Arkaprava Basu
随着不同存储技术的出现，存储器变得越来越异构，从PCM、STT-RAM和忆阻器等非易失性存储器到HBM等3D堆叠存储器。在这样的系统中，数据是跨由不同技术支持的存储器区域迁移的，以获得更好的整体性能。有效的移徙机制是这种制度的先决条件。关于操作系统引导的页面迁移的先前工作集中于迁移什么数据和/或何时迁移。在这项工作中，我们证明了需要调查另一个维度-迁移多少。具体来说，我们表明，在一个单一的迁移操作（称为“迁移粒度”）迁移的数据量是至关重要的整体性能。通过对真实的硬件的分析，我们进一步表明，不同的应用程序受益于不同的迁移粒度，由于其不同的内存访问特性。由于这种首选的迁移粒度可能是未知的先验，我们提出了一种新的方案来推断这对于任何给定的应用程序在运行时。当在Linux操作系统中实现时，在当前硬件上运行时，性能比具有固定迁移粒度的基线提高了36%。
Memory is becoming increasingly heterogeneous with the emergence of disparate memory technologies ranging from non-volatile memories like PCM, STT-RAM, and memristors to 3D-stacked memories like HBM. In such systems, data is of ten migrated across memory regions backed by different technologies for better overall performance. An effective migration mechanism is a prerequisite in such systems. Prior works on OS-directed page migration have focused on what data to migrate and/or on when to migrate. In this work, we demonstrate the need to investigate another dimension -- how much to migrate. Specifically, we show that the amount of data migrated in a single migration operation (called "migration granularity") is vital to the overall performance. Through analysis on real hardware, we further show that different applications benefit from different migration granularities, owing to their distinct memory access characteristics. Since this preferred migration granularity may not be known a priori, we propose a novel scheme to infer this for any given application at runtime. When implemented in the Linux OS, running on a current hardware, the performance improved by up to 36% over a baseline with a fixed migration granularity.