Abstract
Graphics Processing Units (GPUs) are widely spread nowadays due to their parallel processing capabilities. Leveraging these hardware features is particularly important for computationally expensive tasks and workloads. Prominent use cases are optimization problems and simulations that can be parallelized and tuned for these architectures. In the general domain of simulations (numerical and discrete), the overall logic is split into several components that are executed one after another. These components need step-size information which determines the number of steps (e.g. the elapsed time) they have to perform. Small step sizes are often required to ensure a valid simulation result with respect to precision and constraint correctness. Unfortunately, they are often the main bottleneck of the simulation. In this paper, we introduce a new and generic way of realizing high-performance simulations with multiple components using adaptive time steps on GPUs. Our method relies on a code-analysis phase that resolves data dependencies between different components. This knowledge is used to generate specially-tuned execution kernels that encapsulate the underlying component logic. An evaluation on our simulation benchmarks shows that we are able to considerably improve runtime performance compared to prior work.
This work was supported by the SINTEG-project DESIGNETZ funded by the German Federal Ministry of Economic Affairs and Energy (BMWi) under the grant 03SIN222.
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Notes
- 1.
A single simulation iteration is commonly referred to as a simulation step.
- 2.
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The authors would like to thank T. Schmeyer, A. Bosch and J. Bayer for their feedback on the paper, even in the scope of very challenging and stressful times.
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Köster, M., Groß, J., Krüger, A. (2020). High-Performance Simulations on GPUs Using Adaptive Time Steps. In: Qiu, M. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2020. Lecture Notes in Computer Science(), vol 12452. Springer, Cham. https://doi.org/10.1007/978-3-030-60245-1_26
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