A complexity-based classification for multiprocessor synchronization

被引：3
|
作者：
Ellen, Faith [1 ]
Gelashvili, Rati [1 ]
Shavit, Nir [2 ,3 ]
Zhu, Leqi [1 ]
机构：
[1] Univ Toronto, Toronto, ON, Canada
[2] MIT, Cambridge, MA 02139 USA
[3] Tel Aviv Univ, Tel Aviv, Israel
来源：
DISTRIBUTED COMPUTING | 2020年 / 33卷 / 02期
基金：
加拿大自然科学与工程研究理事会; 美国国家科学基金会;
关键词：
SPACE COMPLEXITY; CONSENSUS; BOUNDS;
D O I：
10.1007/s00446-019-00361-3
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
For many years, Herlihy's elegant computability-based Consensus Hierarchy has been our best explanation of the relative power of various objects. Since real multiprocessors allow the different instructions they support to be applied to any memory location, it makes sense to consider combining the instructions supported by different objects, rather than considering collections of different objects. Surprisingly, this causes Herlihy's computability-based hierarchy to collapse. In this paper, we suggest an alternative: a complexity-based classification of the relative power of sets of multiprocessor synchronization instructions, captured by the minimum number of memory locations of unbounded size that are needed to solve obstruction-free consensus when using different sets of instructions.
引用
收藏
页码：125 / 144
页数：20
相关论文
共 50 条
  • [1] A complexity-based classification for multiprocessor synchronization
    Faith Ellen
    Rati Gelashvili
    Nir Shavit
    Leqi Zhu
    [J]. Distributed Computing, 2020, 33 : 125 - 144
  • [2] A Complexity-Based Hierarchy for Multiprocessor Synchronization
    Ellen, Faith
    Gelashvili, Rati
    Shavit, Nir
    Zhu, Leqi
    [J]. PROCEEDINGS OF THE 2016 ACM SYMPOSIUM ON PRINCIPLES OF DISTRIBUTED COMPUTING (PODC'16), 2016, : 289 - 298
  • [3] Complexity-based parallel rule induction for multiclass classification
    Asadi, Shahrokh
    Shahrabi, Jamal
    [J]. INFORMATION SCIENCES, 2017, 380 : 53 - 73
  • [4] Analysis of a complexity-based pruning scheme for classification trees
    Nobel, AB
    [J]. IEEE TRANSACTIONS ON INFORMATION THEORY, 2002, 48 (08) : 2362 - 2368
  • [5] Data complexity-based dynamic ensembling of SVMs in classification
    Sowkarthika, B.
    Gyanchandani, Manasi
    Wadhvani, Rajesh
    Shukla, Sanyam
    [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2023, 216
  • [6] COMPLEXITY-BASED ANALYSIS OF BRAINS' SYNCHRONIZATION IN HUMAN-HUMAN INTERACTION
    Ahamed, Mohamed Rasmi Ashfaq
    Babini, Mohammad Hossein
    Pakniyat, Najmeh
    Namazi, Hamidreza
    [J]. FRACTALS-COMPLEX GEOMETRY PATTERNS AND SCALING IN NATURE AND SOCIETY, 2020, 28 (07)
  • [7] Complexity-based Steganalysis
    Manoharan, Sathiamoorthy
    Master, Aquib
    Speidel, Ulrich
    [J]. 2014 INTERNATIONAL SYMPOSIUM ON INFORMATION THEORY AND ITS APPLICATIONS (ISITA), 2014, : 40 - 44
  • [8] COMPLEXITY-BASED INDUCTION
    CONKLIN, D
    WITTEN, IH
    [J]. MACHINE LEARNING, 1994, 16 (03) : 203 - 225
  • [9] COMPLEXITY-BASED CLASSIFICATION OF THE CORONAVIRUS DISEASE (COVID-19)
    Namazi, Hamidreza
    Kulish, Vladimir V.
    [J]. FRACTALS-COMPLEX GEOMETRY PATTERNS AND SCALING IN NATURE AND SOCIETY, 2020, 28 (05)
  • [10] Complexity-based classification of EEG signal in normal subjects and patients with epilepsy
    Namazi, Hamidreza
    Aghasian, Erfan
    Ala, Tirdad Seifi
    [J]. TECHNOLOGY AND HEALTH CARE, 2020, 28 (01) : 57 - 66
12345