Characterizing and Optimizing Hybrid DRAM-PM Main Memory System with Application Awareness
Yongfeng Wanga, Yinjin Fub, Yubo Liuc, Zhiguang Chend and Nong Xiaoe
School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
aYongfeng.Wang@nscc-gz.cn
bYinjin.Fu@nscc-gz.cn
cYubo.Liu@nscc-gz.cn
dZhiguang.Chen@nscc-gz.cn
exiaon6@mail.sysu.edu.cn
ABSTRACT
Persistent memory (PM) has always been used in combination with DRAM to configure hybrid main memory systems that can obtain both the high performance of DRAM and large capacity of PM. There are critical management challenges in data placement, memory concurrency and workload scheduling for the concurrent execution of multiple application workloads. But the non-negligible performance gap between DRAM and PM makes the existing application-agnostic management strategies inefficient in reaching the full potential of hybrid memory. In this paper, we propose a series of application aware optimization strategies, including application aware data placement, adaptive thread allocation and inter-application interference avoiding, to improve the concurrent performance of different application workloads on hybrid memory. Finally, we provide the performance evaluation for our application aware solutions on real hybrid memory hardware with some comprehensive benchmark suites. Our experimental results show that the duration of multi-application concurrent execution on hybrid memory can be reduced by at most 60.7% for application aware data placement, 37.7% for adaptive thread allocation and 34.8% for workload scheduling with interapplication interference avoiding, respectively. And the additive effects of all these three optimization methods can reach 62.8% performance improvement with negligible overheads.
