Saurabh V. Pendse

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First-principles based predictive understanding of complex , dynamic physical phenomena, such as regional precipitation or hurricane intensity and frequency, is quite limited due to the lack of complete phenomeno-logical models underlying their physics. To address this gap, hypothesis-driven, manually-constructed, conceptual hurricane models and models for(More)
Current peta-scale data analytics frameworks suffer from a significant performance bottleneck due to an imbalance between their enormous computational power and limited I/O bandwidth. Using data compression schemes to reduce the amount of I/O activity is a promising approach to addressing this problem. In this paper, we propose a hybrid framework for(More)
The process of scientific data analysis in high-performance computing environments has been evolving along with the advancement of computing capabilities. With the onset of exascale computing, the increasing gap between compute performance and I/O bandwidth has rendered the traditional method of post-simulation processing a tedious process. Despite the(More)
—The ability to efficiently handle massive amounts of data is necessary for the continuing development towards ex-ascale scientific data-mining applications and database systems. Unfortunately, recent years have shown a growing gap between the size and complexity of data produced from scientific applications and the limited I/O bandwidth available on modern(More)
Scientific data analytics in high-performance computing environments has been evolving along with the advancement of computing capabilities. With the onset of exascale computing, the increasing gap between compute performance and I/O bandwidth has rendered the traditional post-simulation processing a tedious process. Despite the challenges due to increased(More)
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