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Global climate modeling not only requires computation capabilities, but also brings tough challenges for data storage systems. The input and output data sets generally require hundreds or even thousands of terabytes storage. Therefore, storage reduction methods, such as content deduplication and various data compression methods, are extremely important for reducing the storage size requirement in...
Climate modeling data are usually multidimen-sional arrays of floating-point numbers. These arrays typically have two or three spatial dimensions and one temporal dimension, describing the evolvement of climate variables in a time span. With the advances of high performance computing, the volume of climate data is expanding exponentially, bringing tough challenges for climate data archiving and sharing...
With the rapid advances in supercomputing and numerical simulations, the output data of scientific computing is expanding rapidly, bringing tough challenges for data sharing and data archiving. Data compression can mitigate these challenges by reducing the size of the data to be stored or transferred. However, data compression has to achieve a good balance between compression ratios and throughput,...
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