Abstract
Binomial tree model is often used for option pricing in the financial market. According to this method, it is rather expensive to obtain high accurate option price. Although existing methods running on CPU clusters have improved the efficiency significantly, there is still a great gap between the real performance and the desired. In this paper, we parallelize this model on CUDA to further improve the efficiency. We optimize our method according to principles of memory hierarchy and extend it to support multiple GPUs. Experiments on single Tesla C1060 GPU chip show an average of 285\(\times \) speedup compared to the result on single CPU node. Furthermore, for the data size of 64 K, GPU performance has reached 315 Gflops, which outperforms the earlier version on the Sun station by a factor of about 100\(\times \). The maximum performance reached with 108 GPU nodes is 30 Tflops.
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Acknowledgments
This work is supported by the National High-Tech Research and Development Plan of China under Grant Nos. 2006AA01A114, 2007AA120502, and Shenz-hen Innovation Technology Program under Grant No. SY200806300211A.
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Gui, Y., Feng, S., Wen, G., Zhang, G., Wan, Y., Liu, T. (2013). High Performance Implementation of Binomial Option Pricing Using CUDA. In: Yuen, D., Wang, L., Chi, X., Johnsson, L., Ge, W., Shi, Y. (eds) GPU Solutions to Multi-scale Problems in Science and Engineering. Lecture Notes in Earth System Sciences. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16405-7_12
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DOI: https://doi.org/10.1007/978-3-642-16405-7_12
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