Dynamically Adapting Page Migration Policies Based on Applications’ Memory Access Behaviors

There have been numerous studies on heterogeneous memory systems comprised of faster DRAM (e.g., 3D stacked HBM or HMC) and slower non-volatile memories (e.g., PCM, STT-RAM). However, most of these studies focused on static policies for managing data placement and migration among the different memory devices. These policies are based on the average behavior across a range of applications. Results show that these techniques do not always result in higher performance when compared to systems that do not migrate data across the devices: some applications show performance gains, but other applications show performance losses. It is possible to utilize offline analyses to identify which applications benefit from page migration (migration friendly) and use page migration only with those applications. However, we observed that several applications exhibit both migration friendly and migration unfriendly behaviors during different phases of execution supporting a need for adaptive page migration techniques. We introduce and evaluate techniques that dynamically adapt to the behavior of applications and either reduce or increase migrations, or even halt migrations. Our adaptive techniques show performance gains for both migration friendly (on average of 81% over no migrations) and unfriendly workloads (by an average of 3%): it should be remembered that previous migration techniques resulted in performance losses for unfriendly workloads.

Reliability-aware Garbage Collection for Hybrid HBM-DRAM Memories

Emerging workloads in cloud and data center infrastructures demand high main memory bandwidth and capacity. Unfortunately, DRAM alone is unable to satisfy contemporary main memory demands. High-bandwidth memory (HBM) uses 3D die-stacking to deliver 4–8× higher bandwidth. HBM has two drawbacks: (1) capacity is low, and (2) soft error rate is high. Hybrid memory combines DRAM and HBM to promise low fault rates, high bandwidth, and high capacity. Prior OS approaches manage HBM by mapping pages to HBM versus DRAM based on hotness (access frequency) and risk (susceptibility to soft errors). Unfortunately, these approaches operate at a coarse-grained page granularity, and frequent page migrations hurt performance. This article proposes a new class of reliability-aware garbage collectors for hybrid HBM-DRAM systems that place hot and low-risk objects in HBM and the rest in DRAM. Our analysis of nine real-world Java workloads shows that: (1) newly allocated objects in the nursery are frequently written, making them both hot and low-risk, (2) a small fraction of the mature objects are hot and low-risk, and (3) allocation site is a good predictor for hotness and risk. We propose RiskRelief, a novel reliability-aware garbage collector that uses allocation site prediction to place hot and low-risk objects in HBM. Allocation sites are profiled offline and RiskRelief uses heuristics to classify allocation sites as DRAM and HBM. The proposed heuristics expose Pareto-optimal trade-offs between soft error rate (SER) and execution time. RiskRelief improves SER by 9× compared to an HBM-Only system while at the same time improving performance by 29% compared to a DRAM-Only system. Compared to a state-of-the-art OS approach for reliability-aware data placement, RiskRelief eliminates all page migration overheads, which substantially improves performance while delivering similar SER. Reliability-aware garbage collection opens up a new opportunity to manage emerging HBM-DRAM memories at fine granularity while requiring no extra hardware support and leaving the programming model unchanged.