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Article
Open AccessDiscussion on “Saving Storage in Climate Ensembles: A Model-Based Stochastic Approach”
We thank the authors for this interesting paper that highlights important ideas and concepts for the future of climate model ensembles and their storage, as well as future uses of stochastic emulators. Stochas...
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Chapter and Conference Paper
Fulfilling the Promises of Lossy Compression for Scientific Applications
Many scientific simulations, machine/deep learning applications and instruments are in need of significant data reduction. Error-bounded lossy compression has been identified as one solution and has been teste...
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Chapter and Conference Paper
Understanding and Improving the Trust in Results of Numerical Simulations and Scientific Data Analytics
With ever-increasing execution scale of parallel scientific simulations, potential unnoticed corruptions to scientific data during simulation make users more suspicious about the correctness of floating-point ...
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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...
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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...
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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...
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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...
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Chapter and Conference Paper
Efficient Time Series Data Classification and Compression in Distributed Monitoring
As a key issue in distributed monitoring, time series data are a series of values collected in terms of sequential time stamps. Requesting them is one of the most frequent requests in a distributed monitoring ...
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Chapter and Conference Paper
A Composite-Service Authorization Prediction Platform for Grid Environment
In workflow and grid environment, the security challenges with the appearance of composite service increasingly become more severe than before especially to the traditional static access control model and dyna...
