Enabling Data Analytics Workflows Using Node-Local Storage

Authors: Tu Mai Anh Do (University of Southern California, Information Sciences Institute), Ming Jiang (Lawrence Livermore National Laboratory), Brian Gallagher (Lawrence Livermore National Laboratory), Albert Chu (Lawrence Livermore National Laboratory), Cyrus Harrison (Lawrence Livermore National Laboratory), Karan Vahi (University of Southern California, Information Sciences Institute), Ewa Deelman (University of Southern California, Information Sciences Institute)

Abstract: The convergence of high-performance computing (HPC) and Big Data is a necessity with the push toward extreme-scale computing. As HPC simulations become more complex, the analytics need to process larger amounts of data, which poses significant challenges for coupling HPC simulations with Big Data analytics. This poster presents a novel node-local approach that uses a workflow management system (WMS) to enable the coupling between the simulations and the analytics in scientific workflows by leveraging node-local non-volatile random-access memory (NVRAM).

Best Poster Finalist (BP): no

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