Abstract
Monte Carlo simulation is an important method of uncertainty quantification in a variety of scientific and engineering disciplines. Computational scalability is a critical problem in Monte Carlo implementation for many dynamical systems. However, new high-throughput computing architectures such as GPU’s are enabling realizations of Monte Carlo that exhibit 1–2 orders-of-magnitude runtime reductions over comparable serial methods. This chapter outlines the basic process of Monte Carlo implementation on GPU’s including optimization techniques and cost-benefit considerations. Example source code listings are provided for Monte Carlo simulation of a point-mass in atmospheric flight. Runtime results are provided demonstrating significant runtime reductions compared to serial CPU-based implementations.
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Rogers, J. (2014). Monte Carlo Simulation of Dynamic Systems on GPU’s. In: Kindratenko, V. (eds) Numerical Computations with GPUs. Springer, Cham. https://doi.org/10.1007/978-3-319-06548-9_15
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DOI: https://doi.org/10.1007/978-3-319-06548-9_15
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