2021/03/16 by Sandeep Kumar, Aravinda Prasad, Kumar, Sandeep +5
Computer Science · #Advanced Data Storage Technologies #Cloud Computing and Resource Management #Distributed #FOS: Computer and information sciences #Operating Systems (cs.OS) #Parallel #Parallel Computing and Optimization Techniques #Performance (cs.PF) #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.2103.10779
openalex publication_date 2021/03/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Modern enterprise servers are increasingly embracing tiered memory systems with a combination of low latency DRAMs and large capacity but high latency non-volatile main memories (NVMMs) such as Intel's Optane DC PMM. Prior works have focused on efficient placement and migration of data on a tiered memory system, but have not studied the optimal placement of page tables. Explicit and efficient placement of page tables is crucial for large memory footprint applications with high TLB miss rates because they incur dramatically higher page walk latency when page table pages are placed in NVMM. We show that (i) page table pages can end up on NVMM even when enough DRAM memory is available and (ii) page table pages that spill over to NVMM due to DRAM memory pressure are not migrated back later when memory is available in DRAM. We study the performance impact of page table placement in a tiered memory system and propose an efficient and transparent page table management technique that (i) applies different placement policies for data and page table pages, (ii) introduces a differentiating policy for page table pages by placing a small but critical part of the page table in DRAM, and (iii) dynamically and judiciously manages the rest of the page table by transparently migrating the page table pages between DRAM and NVMM. Our implementation on a real system equipped with Intel's Optane NVMM running Linux reduces the page table walk cycles by 12% and total cycles by 20% on an average. This improves the runtime by 20% on an average for a set of synthetic and real-world large memory footprint applications when compared with various default Linux kernel techniques.