Page 439 - 《软件学报》2025年第5期
P. 439
张志国 等: PLTree: 一个高性能持久化内存学习索引 2339
我们将继续探索如何更有效地降低 PLTree 的空间开销, 力求在空间利用和性能之间取得更好的平衡, 并进一步探
讨优化并发控制机制, 以期在提升并发性能的同时, 保证数据的一致性与安全性.
References:
[1] Lu BT, Ding JL, Lo E, Minhas UF, Wang TZ. APEX: A high-performance learned index on persistent memory. Proc. of the VLDB
Endowment, 2021, 15(3): 597–610. [doi: 10.14778/3494124.3494141]
TM
[2] 3D XPoint : A breakthrough in non-volatile memory technology. https://www.intel.com/content/www/us/en/architecture-and-
technology/intel-micron-3d-xpoint-webcast.html
[3] Lersch L, Hao XP, Oukid I, Wang TZ, Willhalm T. Evaluating persistent memory range indexes. Proc. of the VLDB Endowment, 2019,
13(4): 574–587. [doi: 10.14778/3372716.3372728]
+
[4] Hwang D, Kim WH, Won Y, Nam B. Endurable transient inconsistency in Byte-addressable persistent B -tree. In: Proc. of the 16th
USENIX Conf. on File and Storage Technologies. Oakland: USENIX Association, 2018. 187–200.
[5] Yang J, Wei QS, Chen C, Wang CD, Yong KL, He BS. NV-Tree: Reducing consistency cost for NVM-based single level systems. In:
Proc. of the 13th USENIX Conf. on File and Storage Technologies. Santa Clara: USENIX Association, 2015. 167–181.
[6] Oukid I, Lasperas J, Nica A, Willhalm T, Lehner W. FPTree: A hybrid SCM-DRAM persistent and concurrent B-tree for storage class
memory. In: Proc. of the 2016 Int’l Conf. on Management of Data. San Francisco: ACM, 2016. 371–386. [doi: 10.1145/2882903.
2915251]
[7] Liu JH, Chen SN, Wang LJ. LB+trees: Optimizing persistent index performance on 3DXPoint memory. Proc. of the VLDB Endowment,
2020, 13(7): 1078–1090. [doi: 10.14778/3384345.3384355]
+
[8] Zhang BW, Zheng S, Qi ZL, Huang LP. NBTree: A lock-free pm-friendly persistent B -tree for eADR-enabled PM systems. Proc. of the
VLDB Endowment, 2022, 15(6): 1187–1200. [doi: 10.14778/3514061.3514066]
[9] Lee SK, Lim KH, Song H, Nam B, Noh SH. WORT: Write optimal radix tree for persistent memory storage systems. In: Proc. of the 15th
USENIX Conf. on File and Storage Technologies. Santa Clara: USENIX Association, 2017. 257–270.
[10] Ma SN, Chen K, Chen SM, Liu MX, Zhu JL, Kang HB, Wu YW. ROART: Range-query optimized persistent art. In: Proc. of the 19th
USENIX Conf. on File and Storage Technologies. USENIX Association, 2021. 1–16.
[11] Kim WH, Krishnan RM, Fu XW, Kashyap S, Min C. PACTree: A high performance persistent range index using pac guidelines. In: Proc.
of the 28th ACM SIGOPS ACM Symp. on Operating Systems Principles. Virtual Event: ACM, 2021. 424–439. [doi: 10.1145/3477132.
3483589]
[12] Zhou XJ, Shou LD, Chen K, Hu W, Chen G. DPTree: Differential indexing for persistent memory. Proc. of the VLDB Endowment, 2019,
13(4): 421–434. [doi: 10.14778/3372716.3372717]
+
[13] Chen YM, Lu YY, Fang KD, Wang Q, Shu JW. uTree: A persistent B -tree with low tail latency. Proc. of the VLDB Endowment, 2020,
13(12): 2634–2648. [doi: 10.14778/3407790.3407850]
[14] Arulraj J, Levandoski J, Minhas UF, Larson PA. BzTree: A high-performance latch-free range index for non-volatile memory. Proc. of
the VLDB Endowment, 2018, 11(5): 553–565. [doi: 10.1145/3187009.3164147]
[15] Debnath B, Haghdoost A, Kadav A, Khatib MG, Ungureanu C. Revisiting hash table design for phase change memory. ACM SIGOPS
Operating Systems Review, 2016, 49(2): 18–26. [doi: 10.1145/2883591.2883597]
[16] Zuo PF, Hua Y, Wu J. Level hashing: A high-performance and flexible-resizing persistent hashing index structure. ACM Trans. on
Storage, 2019, 15(2): 13. [doi: 10.1145/3322096]
[17] Nam M, Cha H, Choi YR, Noh SH, Nam B. Write-optimized dynamic hashing for persistent memory. In: Proc. of the 17th USENIX
Conf. on File and Storage Technologies. Boston: USENIX Association, 2019. 31–44.
[18] Lu BT, Hao XP, Wang TZ, Lo E. Dash: Scalable hashing on persistent memory. Proc. of the VLDB Endowment, 2020, 13(8):
1147–1161. [doi: 10.14778/3389133.3389134]
[19] Hu DK, Chen ZW, Che WK, Sun JH, Chen H. Halo: A hybrid PMEM-DRAM persistent hash index with fast recovery. In: Proc. of the
2022 Int’l Conf. on Management of Data. Philadelphia: ACM, 2022. 1049–1063. [doi: 10.1145/3514221.3517884]
[20] Vogel L, van Renen A, Imamura S, Giceva J, Neumann T, Kemper A. Plush: A write-optimized persistent log-structured hash-table. Proc.
of the VLDB Endowment, 2022, 15(11): 2895–2907. [doi: 10.14778/3551793.3551839]
[21] Kraska T, Beutel A, Chi EH, Dean J, Polyzotis N. The case for learned index structures. In: Proc. of the 2018 Int’l Conf. on Management
of Data. Houston: ACM, 2018. 489–504. [doi: 10.1145/3183713.3196909]
[22] Zhang Z, Jin PQ, Xie XK. Learned indexes: Current situations and research prospects. Ruan Jian Xue Bao/Journal of Software, 2021,