Malakar, P., Vishwanath, V., Munson, T., Knight, C., Hereld, M., Leyffer, S. and Papka, M.E. (2015) Optimal Scheduling of In-Situ Analysis for Large-Scale Scientific Simulations. 2015 SC-International Conference for High Performance Computing, Networking, Storage and Analysis, Austin, TX, 15-20 November 2015, 1-11.
https://doi.org/10.1145/2807591.2807656

has been cited by the following article:

• TITLE: A Data Analysis Framework for Earth System Simulation within an In-Situ Infrastructure

  AUTHORS: D. Wang, X. Luo, F. Yuan, N. Podhorszki

  KEYWORDS: In-Situ Data Analysis, Source Code Analysis, Data Staging, ADIOS, Earth System Model, Machine Learning, SciKit-Learn, E3SM

  JOURNAL NAME: Journal of Computer and Communications, Vol.5 No.14, December 29, 2017

  ABSTRACT: This paper presents a generic procedure to implement a scalable and high performance data analysis framework for large-scale scientific simulation within an in-situ infrastructure. It demonstrates a unique capability for global Earth system simulations using advanced computing technologies (i.e., automated code analysis and instrumentation), in-situ infrastructure (i.e., ADIOS) and big data analysis engines (i.e., SciKit-learn). This paper also includes a useful case that analyzes a globe Earth System simulations with the integration of scalable in-situ infrastructure and advanced data processing package. The in-situ data analysis framework can provides new insights on scientific discoveries in multiscale modeling paradigms.