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Wang, D., Pei, Y., Hernandez, O., Wu, W., Yao, Z., Kim, Y., Wolfe, M. and Kitchen, R. (2017) Compiler Technologies for Understanding Legacy Scientific Code: A Case Study on an ACME Land Module. Procedia Computer Science, 108, 2418-2422.
https://doi.org/10.1016/j.procs.2017.05.264

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.