Monte Carlo simulation is well-suited for GPU acceleration due to its highly parallel nature. GPUs provide lower cost and higher performance than CPUs for Monte Carlo applications. Numerical libraries for GPUs allow developers to focus on their models rather than reimplementing basic components. NAG has developed GPU libraries including random number generators and is working with financial institutions to apply Monte Carlo simulations to problems in finance.

7. Higher order methods – 1 (work by Kai Zhang, University of Warwick, UK)

8. Higher order methods – 2

9. Numerical example – 1

10. Numerical example – 1a

11. Numerical example – 1b

12. Numerical example – 2a

13. Numerical example – 2b

16. Current GPU architecture – e.g. NVIDIA Tesla

18. Tesla C1060 memory (from: M A Martinez-del-Amor et al. (2008) based on E Lindholm et al. (2008))

35. NAG Routines for GPUs

36. nag_gpu_mrg32k3a_uniform

37. nag_gpu_mrg32k3a_uniform

38. nag_gpu_mrg32k3a_uniform

40. NAG Routines for GPUs

41. nag_gpu_mrg32k3a_next_uniform

42. nag_gpu_mrg32k3a_next_uniform

46. BNP Paribas Results – local vol example