IEEE Letters of the Computer Society

Scope Statement

IEEE Letters of the Computer Society (LOCS) is a rigorously peer-reviewed forum for rapid publication of brief articles describing high-impact results in all areas of interest to the IEEE Computer Society.

Additional Information

Topics include, but are not limited to:

  • software engineering and design;
  • information technology;
  • software for IoT, embedded, and cyberphysical systems;
  • cybersecurity and secure computing;
  • autonomous systems;
  • machine intelligence;
  • parallel and distributed software and algorithms;
  • programming environments and languages;
  • computer graphics and visualization;
  • services computing;
  • databases and data-intensive computing;
  • cloud computing and enterprise systems;
  • hardware and software test technology.

From Issue #1 (Jan-June 2018)

DuoModel: Leveraging Reduced Model for Data Reduction and Re-Computation on HPC Storage

By Huizhang Luo, Qing Liu, Zhenbo Qiao, Jinzhen Wang, Mengxiao Wang, and Hong Jiang

Featured article thumbnail imageHigh-performance computing (HPC) applications generate large amounts of floating-point data that need to be stored and analyzed efficiently to extract the insights and advance knowledge discovery. With the growing disparities between compute and I/O, optimizing the storage stack alone may not suffice to cure the I/O problem. There has been a strong push in the HPC communities to perform data reduction before data is transmitted to storage in order to lower the I/O cost. However, as of now, neither lossless nor lossy compressors can achieve the adequate reduction ratio that is desired by applications. This paper proposes DuoModel, a new approach that leverages the similarity between the full and reduced application models, and further improve the data reduction ratio. DouModel further improves the compression ratio of state-ofthe-art compressors via compressing the differences (termed as delta) between the data products of the two models. For data analytics, the high fidelity data can be re-computed by launching the reduced model and applying the compressed delta. Our evaluations confirm that DuoModel can further push the limit of data reduction while the high fidelity of data is maintained.

download PDF View the PDF of this article      csdl View this issue in the digital library


Editorials and Announcements



Guest Editorials

Editorial Board


Darrell Long, University of California, Santa Cruz

Associate Editors

Dirk Duellmann, CERN

Dan Feng, Huazhong University of Science and Technology

Gary Grider - Los Alamos National Laboratory

Kanchi Gopinath - Indian Institute of Science (IISc), Bangalore

James Hughes, University of California, Santa Cruz

Katia Obraczka - University of California, Santa Cruz

Thomas Johannes Emil Schwarz - Marquette University

Marc Shapiro - Sorbonne-Université–LIP6 & Inria

Kwang Mong Sim - Shenzhen University

Web Editor

Yulai Xie, Huazhong University of Science and Technology

Submitting a Paper to LOCS

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NEW MOPC POLICY: For new submissions effective 15 May 2018, the fee to publish pages in excess of the regular paper limit of four pages will change from $200/page to $220/page.

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LOCS Call-for-Papers Flyer (PDF)

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