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Fast growing large-scale systems enable scientific applications to run at a much larger scale and accordingly produce gigantic volumes of simulation output. Such data imposes a grand challenge to post-processing tasks such as visualization and data analysis, because these tasks are often performed at a host machine that is remotely located and equipped with much less memory and storage resources....
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...
Current I/O techniques have pushed the write performance close to the system peak, but they usually overlook the read side of problem. With the mounting needs of scientific discovery, it is important to provide good read performance for many common access patterns. Such demand requires an organization scheme that can effectively utilize the underlying storage system. However, the mismatch between...
Large scale scientific applications are often bottlenecked due to the writing of checkpoint-restart data. Much work has been focused on improving their write performance. With the mounting needs of scientific discovery from these datasets, it is also important to provide good read performance for many common access patterns, which requires effective data organization. To address this issue, we introduce...
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