ATMem: Adaptive data placement in graph applications on heterogeneous memories

Abstract

Active development in new memory devices, such as nonvolatile memories and high-bandwidth memories, brings heterogeneous memory systems (HMS) as a promising solution for implementing large-scale memory systems with cost, area, and power limitations. Typical HMS consists of a smallcapacity high-performance memory and a large-capacity lowperformance memory. Data placement on such systems plays a critical role in performance optimization. Existing efforts have explored coarse-grained data placement in applications with dense data structures; however, a thorough study of applications that are based on graph data structures is still missing. This work proposes ATMem-a runtime framework for adaptive granularity data placement optimization in graph applications. ATMem consists of a lightweight profiler, an analyzer using a novel m-ary tree-based strategy to identify sampled and estimated critical data chunks, and a highbandwidth migration mechanism using a multi-stage multithreaded approach. ATMem is evaluated in five applications on two HMS hardware, including the Intel Optane byte-addressable NVM and MCDRAM. Experimental results show that ATMem selects 5%-18% data to be placed on high-performance memory and achieves an average of 1.7×-3.4× speedup on NVM-DRAM and 1.2×-2.0× speedup on MCDRAM-DRAM, over the baseline that places all data on the large-capacity memory. On NVM-DRAM, ATMem achieves performance comparable to a full-DRAM system with as low as 9%-54% slowdown.