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  1. No Access

    Chapter and Conference Paper

    Exploration of Pattern-Matching Techniques for Lossy Compression on Cosmology Simulation Data Sets

    Because of the vast volume of data being produced by today’s scientific simulations, lossy compression allowing user-controlled information loss can significantly reduce the data size and the I/O burden. Howev...

    Dingwen Tao, Sheng Di, Zizhong Chen, Franck Cappello in High Performance Computing (2017)

  2. Chapter and Conference Paper

    Computing Just What You Need: Online Data Analysis and Reduction at Extreme Scales

    A growing disparity between supercomputer computation speeds and I/O rates makes it increasingly infeasible for applications to save all results for offline analysis. Instead, applications must analyze and red...

    Ian Foster, Mark Ainsworth, Bryce Allen, Julie Bessac in Euro-Par 2017: Parallel Processing (2017)

  3. Chapter and Conference Paper

    Exploring Partial Replication to Improve Lightweight Silent Data Corruption Detection for HPC Applications

    Silent data corruption (SDC) poses a great challenge for high-performance computing (HPC) applications as we move to extreme-scale systems. If not dealt with properly, SDC has the potential to influence import...

    Eduardo Berrocal, Leonardo Bautista-Gomez, Sheng Di in Euro-Par 2016: Parallel Processing (2016)

  4. No Access

    Article

    Characterizing and modeling cloud applications/jobs on a Google data center

    In this paper, we characterize and model Google applications and jobs, based on a 1-month Google trace from a large-scale Google data center. We address four contributions: (1) we compute the valuable statisti...

    Sheng Di, Derrick Kondo, Franck Cappello in The Journal of Supercomputing (2014)