MemBrain: Automated Application Guidance for Hybrid Memory Systems

Abstract

Computer systems with multiple tiers of memory devices with different latency, bandwidth, and capacity char­acteristics are quickly becoming mainstream. Due to cost and physical limitations, device tiers that enable better performance typically include less capacity. Such heterogeneous memory systems require alternative data management strategies to utilize the capacity-constrained resources efficiently. However, current techniques are often limited because they rely on inflexible hardware caching or manual modifications to source code. This paper introduces MemBrain, a new memory management framework that automates the production and use of data-tiering guidance for applications on hybrid memory systems. MemBrain employs program profiling and source code analysis to enable transparent and efficient data placement across different types of memory. It automatically clusters data with similar expected usage patterns into page-aligned regions of virtual addresses (arenas), and uses offline profile feedback to direct low-level tier assignments for each region. We evaluate MemBrain on an Intel Knights Landing server machine with an upper tier of limited capacity (but higher bandwidth) MCDRAM and a lower tier of conventional DDR4 using a selection of high-bandwidth benchmarks from SPEC CPU 2017 as well as two proxy apps (Lulesh and AMG), and one full scale scientific application (QMCPACK). Our evaluation shows that MemBrain can achieve significant performance and efficiency improvements compared to current guided and unguided management strategies.