FE529 GPU Computing in Finance

Course Catalog Description


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.

Campus Fall Spring Summer
On Campus X
Web Campus


Professor Email Office

More Information

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.

Course Resources


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

Lecture Outline

Topic Reading
Week 1 Introduction to massively parallel programming and CUDA CUDA environment configuration GPU Computing Overview Why is parallel computing important? What is CUDA? Why is it important? Sample example using CUDA
Week 2 Basics of CUDA Thread, block, grid, kernel Thread synchronization, communication, and errors CUDA Programming Model
Week 3 CUDA memories Global, shared and Constant Memory Host & device Copying GPU memory
Week 4 CUDA API CUDA API library Random number generator
Week 5 Simple Matrix Multiplication in CUDA How to set threads, block grid Sample parallel computation
Week 6 CUDA Memory Model using GPU memory more efficiently threads management
Week 7 Performance considerations how to parallel the computing strategy Optimization Using Shared Memory
Week 8 Useful Information on CUDA Tools DUDA runtime librart CUDA cor library
Week 9 Parallel Thread Execution CUDA Architecture Execution methodology
Week 10 ArrayFire Array Fire is a fast software library for GPU computing with an easy-to-use API.
Week 11 CUDA demo I Random number generation Comparison with CPU
Week 12 CUDA demo II Monte Carlo simulation using CUDA Comparison with CPU
Week 13 CUDA demo III Optimal Dynamic Monte Carlo method in option pricing
Week 14 Final Project