Abstract
As general purpose computing on Graphics Processing Units (GPGPU) matures, more complicated scientific applications are being targeted to utilize the data-level parallelism available on a GPU. Implementing physically-based simulation on data-parallel hardware requires preprocessing overhead which affects application performance. We discuss our implementation of physics-based data structures that provide significant performance improvements when used on data-parallel hardware. These data structures allow us to maintain a physics-based abstraction of the underlying data, reduce programmer effort and obtain 6x-8x speedup over previously implemented GPU kernels.
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Mistry, P., Schaa, D., Jang, B., Kaeli, D., Dvornik, A., Meglan, D. (2011). Data Structures and Transformations for Physically Based Simulation on a GPU. In: Palma, J.M.L.M., Daydé, M., Marques, O., Lopes, J.C. (eds) High Performance Computing for Computational Science – VECPAR 2010. VECPAR 2010. Lecture Notes in Computer Science, vol 6449. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19328-6_17
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DOI: https://doi.org/10.1007/978-3-642-19328-6_17
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