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The Sirius 2 project will create the capability to enable progressive analysis. To accomplish this, we will build upon our approach that created the MultiGrid Adaptive Reduction of Data (MGARD…
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The Department of Energy (DOE) leads the world in enabling large-scale high-performance computing as a tool for scientific discovery in a broad range of disciplines. Our newest generation of systems…
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The Department of Energy (DOE) has led the world in enabling large-scale high-performance computing as an indispensable tool for scientific discovery in a broad range of disciplines. Our newest…
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The Sensei project is led by Wes Bethel from Lawrence Berkeley National Laboratory and involves participants from multiple laboratories and industries. This project takes aim at a set of…
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The Scientific Discovery through Advanced Computing (SciDAC) High-Fidelity Boundary Plasma Simulation (HBPS) is putting a strong emphasis on experimental validation, and use of data-science…
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The Phase II objective of this project is to provide fusion scientists an ability to perform in situ analytics using the ADIOS framework. This work will enable scientists to use ADIOS to perform this…
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The ADIOS project.
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The goal of this project is to develop a high-fidelity whole device model (WDM) of magnetically confined fusion plasmas, which is urgently needed to understand and predict the performance of ITER and…