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The increasingly important data-intensive scientific discovery presents a critical question to the high performance computing (HPC) community - how to efficiently support these growing scientific big data applications with HPC systems that are traditionally designed for big compute applications? The conventional HPC systems are computing-centric and designed for computation-intensive applications...
Scientific applications from many problem domains produce and/or access large volumes of data. To support these applications, designers of high-end computing (HEC) systems have greatly increased the capacity of storage systems in recent years. However, because hard disk drives (HDDs) are still the dominant storage device used in HEC storage systems, and because HDD performance has not improved as...
The I/O bottleneck issue has been acknowledged as one of main performance issues of high performance computing (HPC) systems for data-intensive scientific applications, and has attracted intensive studies in recent years. With the enlarging gap between the computing bandwidth and I/O bandwidth in projected next-generation HPC systems, this issue will become even worse. In this paper, we present a...
Parallel and distributed file systems are widely used to provide high throughput in high-performance computing and Cloud computing systems. To increase the parallelism, I/O requests are partitioned into multiple sub-requests (or 'flows') and distributed across different data nodes. Therefore the completion time of an I/O request depends on the slowest sub-request and the performance of file systems...
Given the growing importance of supporting dataintensive sciences and big data applications, an effective HPC I/O solution has become a key issue and has attracted intensive attention in recent years. Active storage has been shown effective in reducing data movement and network traffic as a potential new I/O solution. Existing prototypes and systems, however, are primarily designed for read-intensive...
Scientific datasets and libraries, such as HDF5, ADIOS, and NetCDF, have been used widely in many data-intensive applications. These libraries have their special file formats and I/O functions to provide efficient access to large datasets. Recent studies have started to utilize indexing, subsetting, and data reorganization to manage the increasingly large datasets. In this work, we present an approach...
Parallel file systems have been developed in recent years to ease the I/O bottleneck of high-end computing system. These advanced file systems offer several data layout strategies in order to meet the performance goals of specific I/O workloads. However, while a layout policy may perform well on some I/O workload, it may not perform as well for another. Peak I/O performance is rarely achieved due...
High-end computing (HEC) applications in critical areas of science and technology tend to be more and more data intensive. I/O has become a vital performance bottleneck of modern HEC practice. Conventional HEC execution paradigms, however, are computing-centric for computation intensive applications. They are designed to utilize memory and CPU performance and have inherent limitations in addressing...
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