Banking on Monte Carlo and GPUs, Paris FRANCE
NVIDIA, 12 avenue de l'Arche,
Le Colis'e, B'timent B, 6'me 'tage
92400 Courbevoie (Paris La D'fense)
Click here for full directions to venue
Thursday 28th January 2010
6.00pm ' 8.00pm, Reception to follow
The Numerical Algorithms Group (NAG) and NVIDIA hosted a city seminar for finance professionals. The event provided the opportunity to find out more about how to implement fast, efficient Monte Carlo models as well as an overview of NAG and NVIDIA's products.
The speaker slides are now available, which can be accessed using the links adjacent to the talk details.
6.00-6.15 pm Arrival and Registration
An Overview of GPU Computing in Financial Services
Alastair Houston, NVIDIA.
The presentation will introduce Nvidia and the concept of GPU computing in the context of Financial Services industry. Customer successes will be referenced where dramatic speed-ups in performance have been achieved.
Numerical Excellence in Finance
John Holden, NAG.
The presentation will include product demonstrations relevant to finance. Attendees will gain an understanding of how NAG's mathematical and statistical software can be integrated into many different programs and environments, including Excel, MATLAB (using the NAG Toolbox for MATLAB'), C, C++, and C#.
Monte Carlo Simulation and its Efficient Implementation
Robert Tong, NAG.
Monte Carlo simulation is one of the most important numerical methods in financial derivative pricing and risk management. Due to the increasing sophistication of exotic derivative models, Monte Carlo becomes the method of choice for numerical implementations because of its flexibility in high-dimensional problems. However, the method of discretization of the underlying stochastic differential equation (SDE) has a significant effect on convergence. In addition the choice of computing platform and the exploitation of parallelism offers further efficiency gains. We consider here the effect of higher order discretization methods together with the possibilities opened up by the advent of programmable graphics processing units (GPUs) on the overall performance of Monte Carlo and quasi-Monte Carlo methods.