Data transfer minimization for financial derivative pricing using Monte Carlo simulation with GPU in 5G

作者：Qiu Meikang^*; Cao Diqiu; Su Hai; Gai Keke

来源：International Journal of Communication Systems, 2016, 29(16): 2364-2374.

DOI：10.1002/dac.2959

• data transfer minimization
• Monte Carlo simulation
• financial derivative pricing
• graphics processing unit
• 5G

Mobile devices with an enhanced capability are desired to achieve more efficient and energy-aware wireless communications in the future 5G networks. However, the performance of the expected hardware and networks will face a great challenge in efficiently calculating large data transmissions during the heavy wireless communications with a great number of random data. This paper proposes a novel paradigm for preventing future mobile technology implementations from the restrictions of hardware by using graphics processing units (GPUs). Our proposed mechanism is the first attempt in using GPUs to strengthen the wireless communication capability, which is proved by the experimental research and contributes to mobile technologies in 5G. We apply the Monte Carlo simulation method that is for evaluating the results of algorithms whose output depends on random variables. The experimental results show that Monte Carlo simulation based on GPU can achieve up to 25.9x speed compared with CPU-based implementation.

• 出版日期2016-11-10