In-situ Data Analysis Framework for ACME Land Simulations

Thursday, 17 December 2015
Poster Hall (Moscone South)
Dali Wang1, Cindy Yao2, Yulu Jia2, Chad Steed1 and Scott Atchley1, (1)Oak Ridge National Laboratory, Oak Ridge, TN, United States, (2)University of Tennessee, Knoxville, TN, United States
The realistic representation of key biogeophysical and biogeochemical functions is the fundamental of process-based ecosystem models. Investigating the behavior of those ecosystem functions within real-time model simulation can be a very challenging due to the complex of both model and software structure of an environmental model, such as the Accelerated Climate Model for Energy (ACME) Land Model (ALM). In this research, author will describe the urgent needs and challenges for in-situ data analysis for ALM simulations, and layouts our methods/strategies to meet these challenges. Specifically, an in-situ data analysis framework is designed to allow users interactively observe the biogeophyical and biogeochemical process during ALM simulation. There are two key components in this framework, automatically instrumented ecosystem simulation, in-situ data communication and large-scale data exploratory toolkit. This effort is developed by leveraging several active projects, including scientific unit testing platform, common communication interface and extreme-scale data exploratory toolkit. Authors believe that, based on advanced computing technologies, such as compiler-based software system analysis, automatic code instrumentation, and in-memory data transport, this software system provides not only much needed capability for real-time observation and in-situ data analytics for environmental model simulation, but also the potentials for in-situ model behavior adjustment via simulation steering.