Monte-Carlo Methods on GPGPU with Applications to Mathematical Finance
|Lecture and Exercises
Dr. Benedikt Wilbertz
|30 September, 2013 - 2 October, 2013
See detailed schedule below
Room B 121
|Final Exam||to be announced|
|Morning Session 1||9:00 - 10:30|
|Morning Session 2||11:00 - 12:30|
|Afternoon Session 1||14:00 - 15:30|
|Afternoon Session 2||16:00 - 17:30|
Note: The lecture will take place in a computer equipped room with limited places. A registration for the lecture is required. Please register via email to email@example.com before the registration deadline September 1, 2013
Update: Due to seats still being available, the registration deadline is extended until further notice. Interested students are encouraged to still register.
This course will give an introduction on parallel programming on general purpose graphics devices (GPGPU) using NVIDIAs CUDA architecture. GPGPUs differ from ordinary CPUs by their vast amount of (rather simple) processor cores and therefore allow, when all cores are utilized efficiently, to outperform ordinary CPUs by several orders of magnitude. We will start with a brief overview on the hardware design of CUDA devices and general aspects of multi-threading, before we discuss the generation of random numbers on parallel architectures in detail. In general there are two approaches for this problem: The batch approach, where the challenge lies in determining a sequence of seed values which can be processed within independent streams but still yield in total a series of independent random numbers, and the skip-ahead approach, which aims at modifying a random number algorithm such that it is possible to jump ahead in the original sequence of random numbers. To conclude we will apply above methods for the valuation of derivatives and develop an efficient and numerically stable scheme for Monte-Carlo simulation on GPU devices.
- Format: The content of the course will be divided into Theory (4 x 2h), Practice (4 x 2h), and Exercises (4 x 2h).
- Note: The course will be in English.
Target Participants: Master students of Business Mathematics (Wirtschaftsmathematik).
Pre-requisites: Solid knowledge of C/C++ or Java and basic options pricing theory.
Applicable credits: Business Mathematics (Wirtschaftsmathematik) students will receive 3 ECTS Points upon successful participation that may be attributed to any one of the following modules: WP20, WP22 or WP23.
There will be a written final exam. More details will be announced.