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In today's "Big Data" era, developers have adopted I/O techniques such as MPI-IO, Parallel NetCDF and HDF5 to garner enough performance to manage the vast amount of data that scientific applications require. These I/O techniques offer parallel access to shared datasets and together with a set of optimizations such as data sieving and two-phase I/O to boost I/O throughput. While most of these...
In the era of petascale computing, more scientific applications are being deployed on leadership scale computing platforms to enhance the scientific productivity. Many I/O techniques have been designed to address the growing I/O bottleneck on large-scale systems by handling massive scientific data in a holistic manner. While such techniques have been leveraged in a wide range of applications, they...
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