Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
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Large scale scientific applications generally experience different execution phases at runtime and each phase has different computational and communication requirements. An optimal solution or numerical scheme for one execution phase might not be appropriate for the next phase of the application execution. In this paper we present Physics Aware Programming (PAP) paradigm that supports dynamic changes of the application solution if it optimizes the application performance at runtime. In the PAP approach, the application execution state is periodically monitored and analyzed to identify its current execution phase (state). For each change in the application execution phase, we will exploit the spatial and temporal attributes of the application physics to select the numerical algorithms/solvers that optimize its performance. We have applied our approach to a Ten-Tusscher's model of human ventricular epicardia myocyte paced at a varied cycle length (1000 to 50 ms). At runtime, we recognize the current phase of the heart simulation and based on the detected phase, we adopt the simulation Δt that maximizes the performance and maintains the required accuracy. Our experimental results show that we can achieve a speedup of three orders of magnitude while maintaining the required accuracy.