Introduction

In this course the students will learn the basics of CUDA programming using financial data and applications. They will learn how to use C++, Matlab and R to access the GPU in their computer and to use the Stevens GPU cluster. The course is designed for Nvidia CUDA but the basics are easily transferable to Open CL.

Course Description

Parallel programming using GPU’s is a relatively new area for multithreaded programming. It requires a certain amount of extra knowledge even for the most accomplished programmers. The objective of the course is to provide this extra for our students. Our students will be very well prepared for their future programming and software developing jobs by completing this course. This course is the first of a sequence of advanced programming courses that at the moment do not exist in any financial program at any US institution. This sequence of courses (if realized) in 5 years will make Stevens the top US institution for financial programming. It is easy to see then that the students completing this course will gain unique skills that will put them on top of other graduates.

Course Outcome

After completing the course, students will be able to:

1. Gain basic knowledge of parallel programming;
2. Understand the memory management and data transfer methodology in CUDA
3. Program simple financial models using CUDA platform.

Textbook

Sanders, Jason, and Edward Kandrot. CUDA by example: an introduction to general-purpose GPU programming. Addison-Wesley Professional, 2010. https://developer.nvidia.com/cuda-example

Additional References

Grading Policies

• 40% Homework
• 20% Classwork
• 40% Projects