Branch Prediction and Speculative Execution in Magnetospheric Forecasting

Details Branch Prediction and Speculative Execution in Magnetospheric Forecasting Branch Prediction and Speculative Execution in Magnetospheric Forecasting

Dec 2003

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2003agufmsm51b0522d&link_type=abstract

American Geophysical Union, Fall Meeting 2003, abstract #SM51B-0522

Physics

2722 Forecasting, 2740 Magnetospheric Configuration And Dynamics, 2753 Numerical Modeling

Scientific paper

Recent advances in the development of integrated models of the Sun-Earth environment are placing increasing emphasis on data assimilation schemes that can maximize the intelligence extracted from our sparse sampling of upwind conditions. One of the schemes proposed is Branch Prediction and Speculative Execution, which consists of making probabilistic estimates of current upstream conditions, and distribute among available machines simulations that assume each of the probabilistically estimated states as initial conditions. As the near-Earth space evolves and near-Earth satellite data are compared with the models, some of the speculatively executed simulations will be proved wrong. At that point the machines that were executing them will be reassigned either to new lines of speculative simulation, or to increase the processing power devoted to more promising simulations already executing. The scheme is particularly suited to Space Weather since our upwind early warning sentries can provide only sparse sampling of the incoming solar wind, while the bulk of our monitors, which can provide significantly better coverage, are located close to Earth and provide much shorter lead times. By the time the data come in from the near-Earth monitors, the forecasts of the speculative simulations are already in hand. CALCHAS is a 3D visualization package that integrates models and data, and is used in the above data assimilation scheme. The package is written in Java 3D, and has a modular design, so that different models and datasets, both real-time and historical, can be seamlessly compared using a variety of goodness-of-fit measures. The scheme gives good results when used with particle simulations and WINDMI, a low-dimensional dynamical model of the coupled Magnetosphere-Ionosphere system (Doxas and Horton, Using Branch Prediction and Speculative Execution to Forecast Space Weather, Geospace Environment Modeling meeting, Telluride, CO, 2002). Numerical perturbation studies of global MHD models will also be presented.

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