Exploring Memory Access Similarity to Improve Irregular Application Performance for Distributed Hybrid Memory Systems
Wenjie Liu, Xubin He, Qing Liu · IEEE Transactions on Parallel and Distributed Systems · 2022
With the increasing problem complexity, more irregular applications are deployed on high-performance clusters due to the parallel working paradigm, and yield irregular memory access behaviors across nodes. However, the irregularity of memory access behaviors is not comprehensively studied, which results in low utilization of the integrated hybrid memory system compositing of stacked DRAM and off-chip DRAM. To address this problem, we devise a novel method calledSimilarity-Managed Hybrid Memory System(SM-HMS) to improve the hybrid memory system performance by leveraging the memory access similarity among nodes in a cluster. WithinSM-HMS, two techniques are proposed,Memory Access Similarity MeasuringandSimilarity-based Memory Access Behavior Sharing. To quantify the memory access similarity, memory access behaviors of each node are vectorized, and the distance between two vectors is used as the memory access similarity. The calculated memory access similarity is used to share memory access behaviors precisely across nodes. With the shared memory access behaviors,SM-HMSdivides the stacked DRAM into two sections, thesliding window sectionand theoutlier section. The shared memory access behaviors guide the replacement of thesliding window sectionwhile theoutlier sectionis managed in the LRU manner. Our evaluation results with a set of irregular applications on various clusters consisting of up to 256 nodes have shown thatSM-HMSoutperforms the state-of-the-art approaches,Cameo,Chameleon, andHyrbid2, on job finish time reduction by up to$58.6\%$,$56.7\%$, and$31.3\%$, with$46.1\%$,$41.6\%$, and$19.3\%$on average, respectively.SM-HMScan also achieve up to$98.6\%$($91.9\%$on average) of the ideal hybrid memory system performance.