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Accelerating astrophysical particle simulations with programmable hardware (FPGA and GPU)

R. Spurzem, P. Berczik, G. Marcus, A. Kugel, G. Lienhart, I. Berentzen, R. Manner, R. Klessen, R. Banerjee
University of Heidelberg, Astronomisches Rechen-Institut (ARI-ZAH), Monchhofstr. 12-14, 69120 Heidelberg
Computer Science – Research and Development, Vol. 23, No. 3. (1 June 2009), pp. 231-239.

@article{spurzem2009accelerating,

   title={Accelerating astrophysical particle simulations with programmable hardware (FPGA and GPU)},

   author={Spurzem, R. and Berczik, P. and Marcus, G. and Kugel, A. and Lienhart, G. and Berentzen, I. and M{\”a}nner, R. and Klessen, R. and Banerjee, R.},

   journal={Computer Science-Research and Development},

   volume={23},

   number={3},

   pages={231–239},

   year={2009},

   publisher={Springer}

}

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In a previous paper we have shown that direct gravitational N-body simulations in astrophysics scale very well for moderately parallel supercomputers (order 10–100 nodes). The best balance between computation and communication is reached if the nodes are accelerated by special purpose hardware; in this paper we describe the implementation of particle based astrophysical simulation codes on new types of accelerator hardware (field programmable gate arrays, FPGA, and graphical processing units, GPU). In addition to direct gravitational N-body simulations we also use the algorithmically similar “smoothed particle hydrodynamics” method as test application; the algorithms are used for astrophysical problems as e.g. evolution of galactic nuclei with central black holes and gravitational wave generation, and star formation in galaxies and galactic nuclei. We present the code performance on a single node using different kinds of special hardware (traditional GRAPE, FPGA, and GPU) and some implementation aspects (e.g. accuracy). The results show that GPU hardware for real application codes is as fast as GRAPE, but for an order of magnitude lower price, and that FPGA is useful for acceleration of complex sequences of operations (like SPH). We discuss future prospects and new cluster computers built with new generations of FPGA and GPU cards.
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