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Hierarchical Collective I/O Scheduling for High-Performance Computing

  • Published:2015-09 Issue:3 Volume:2 Page:117-126
  • ISSN:2214-5796
  • Container-title:Big Data Research
  • language:en
  • Short-container-title:Big Data Research

Author:

Liu JialinORCID,Zhuang Yu,Chen Yong

Funder

National Science Foundation

Texas Tech University

Publisher

Elsevier BV

Subject

Information Systems and Management,Computer Science Applications,Information Systems,Management Information Systems

Reference41 articles.

1. Litmus: landslide detection by integrating multiple sources;Aibek Musaev,2014

2. Reducing communication costs in collective I/O in multi-core cluster systems with non-exclusive scheduling;Cha;J. Supercomput.,2012

3. Automatically selecting the number of aggregators for collective I/O operations;Chaarawi,2011

4. LACIO: a new collective I/O strategy for parallel I/O systems;Chen,2011

5. Parallel index and query for large scale data analysis;Chou,2011

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4. Client-side straggler-aware I/O scheduler for object-based parallel file systems;Parallel Computing;2019-02

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