NMST:A Persistent Memory Management Optimization Approach Based on Segment Tree

Abstract

Abstract: The emergence of non-volatile memory (NVM) has led to the innovation of the programming model.The exi-sting programming models based on function library provide ACID characteristics to solve the problem of data consistency for memory system.However,they introduce huge overhead when allocating persistent memory dynamically,thereby degrading the applications’ performance.In this paper,an optimization approach based on segment tree was proposed to speedup persistent memory allocation and management.NMST targets NVM Library (NVML),namely a representative library programming model.Furthermore,an optimized NMST was proposedto mitigate the huge overhead of segment tree in maintaining continuous space by constructing segment tree with multi-granularity leaf nodes.The experimental results show that NMST reduces the latency by 36.9% compared with traditional methods when allocating persistent memory,and the optimized NMST reduces the latency by 43.6%.The results also demonstrate that performance improvement is closely related to the quantity and granularity of persistent memory allocation in programs.

Key words: Non-volatile memory, Persistent memory management, Programming model, Segment tree

CLC Number:

TP391

HOU Ze-yi, WAN Hu, XU Yuan-chao. NMST:A Persistent Memory Management Optimization Approach Based on Segment Tree[J].Computer Science, 2018, 45(7): 78-83.

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