Exploring and Exploiting the Multilevel Parallelism Inside SSDs for Improved Performance and Endurance

被引:146
|
作者
Hu, Yang [1 ]
Jiang, Hong [2 ]
Feng, Dan [1 ]
Tian, Lei [2 ]
Luo, Hao [2 ]
Ren, Chao [1 ]
机构
[1] Huazhong Univ Sci & Technol, Wuhan Natl Lab Optoelect, Sch Comp Sci & Technol, Wuhan 430074, Hubei, Peoples R China
[2] Univ Nebraska, Dept Comp Sci & Engn, Schorr Ctr 217, Lincoln, NE 68588 USA
基金
美国国家科学基金会;
关键词
NAND Flash-based SSD; advanced commands; allocation schemes; internal parallelism; performance; endurance; FLASH; SYSTEM;
D O I
10.1109/TC.2012.60
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Given the multilevel internal SSD parallelism at the different four levels: channel-level, chip-level, die-level, and plane-level, how to exploit these levels of parallelism will directly and significantly impact the performance and endurance of SSDs, which is in turn primarily determined by three internal factors, namely, advanced commands, allocation schemes, and the priority order of exploiting the four levels of parallelism. In this paper, we analyze these internal factors to characterize their impacts, interplay, and parallelism for the purpose of performance and endurance enhancement of SSDs through an in-depth experimental study. We come to the following key conclusions: 1) Different advanced commands provided by Flash manufacturers exploit different levels of parallelism inside SSDs, where they can either improve or degrade the SSD performance and endurance depending on how they are used; 2) Different physical-page allocation schemes employ different advanced commands and exploit different levels of parallelism inside SSDs, giving rise to different performance and endurance impacts; 3) The priority order of using the four levels of parallelism has the most significant performance and endurance impact among the three internal factors. The optimal priority order of using the four levels of parallelism in SSDs is found to be: 1) the channel-level parallelism; 2) the die-level parallelism; 3) the plane-level parallelism; and 4) the chip-level parallelism.
引用
收藏
页码:1141 / 1155
页数:15
相关论文
共 50 条
  • [41] Exploiting Hyper-Loop Parallelism in Vectorization to Improve Memory Performance on CUDA GPGPU
    Xu, Shixiong
    Gregg, David
    2015 IEEE TRUSTCOM/BIGDATASE/ISPA, VOL 3, 2015, : 53 - 60
  • [42] Exploring Various Levels of Parallelism in High-Performance CRC Algorithms
    Chi, Mucong
    He, Dazhong
    Liu, Jun
    IEEE ACCESS, 2019, 7 : 32315 - 32326
  • [43] Exploring the Performance of Multilevel Modeling and Poststratification with Eurobarometer Data
    Toshkov, Dimiter
    POLITICAL ANALYSIS, 2015, 23 (03) : 455 - 460
  • [44] Exploiting Subarrays Inside a Bank to Improve Phase Change Memory Performance
    Yue, Jianhui
    Zhu, Yifeng
    DESIGN, AUTOMATION & TEST IN EUROPE, 2013, : 386 - 391
  • [45] EXPLOITING FUNCTIONAL PARALLELISM OF POWER2 TO DESIGN HIGH-PERFORMANCE NUMERICAL ALGORITHMS
    AGARWAL, RC
    GUSTAVSON, FG
    ZUBAIR, M
    IBM JOURNAL OF RESEARCH AND DEVELOPMENT, 1994, 38 (05) : 563 - 576
  • [46] Accelerating large-scale multi-scalar multiplication in Zk-SNARK through exploiting its multilevel parallelism
    Wang, Ning
    Wang, Feng
    Hua, Pengcheng
    Zhao, Xu
    Chai, Zhilei
    INTEGRATION-THE VLSI JOURNAL, 2025, 100
  • [47] Performance Improved Multilevel Inverter with Selective Harmonic Elimination
    Mamatha, P.
    Venkatesh, Challa
    2017 INTERNATIONAL CONFERENCE ON RECENT TRENDS IN ELECTRICAL, ELECTRONICS AND COMPUTING TECHNOLOGIES (ICRTEECT), 2017, : 133 - 138
  • [48] Reducing Flash Memory Write Traffic by Exploiting a Few MBs of Capacitor-Powered Write Buffer Inside Solid-State Drives (SSDs)
    Chen, Xubin
    Li, Yin
    Zhang, Tong
    IEEE TRANSACTIONS ON COMPUTERS, 2019, 68 (03) : 426 - 439
  • [49] Improving Performance of Simple Cores by Exploiting Loop-Level Parallelism through Value Prediction and Reconfiguration
    Suri, Tameesh
    Aggarwal, Aneesh
    CF'09: CONFERENCE ON COMPUTING FRONTIERS & WORKSHOPS, 2009, : 151 - 160
  • [50] Permutation Index: Exploiting Data Skew for Improved Query Performance
    Zhang, Wangda
    Ross, Kenneth A.
    2020 IEEE 36TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2020), 2020, : 1982 - 1985