Quantitative Analysis
Parallel Processing
Numerical Analysis
C++ Multithreading
Python for Excel
Python Utilities

I. Introduction into GPU programming.
1. What are GPU and CUDA?
2. Selecting GPU.
3. Setting up development environment.
4. Combined use of Cuda, C++ and boost::python.
5. Debugging of boost::python binary using Visual Studio.
6. Debugging of boost::python/Cuda binary using Visual Studio.
7. Using printf in device code.
II. Exception safe dynamic memory handling in Cuda project.
III. Calculation of partial sums in parallel.
IV. Manipulation of piecewise polynomial functions in parallel.
V. Manipulation of localized piecewise polynomial functions in parallel.
Downloads. Index. Contents.

Selecting GPU.

here is a slight chance that your existing graphic card supports CUDA.

You cannot buy any GPU, install it and expect it to work. Besides the obvious: motherboard compatibility, the most restrictive reason is capacity of power module of your PC. Most home computers are designed with energy efficiency in mind. The power module probably has only a slight excess capacity over requirements of hardware configuration.

Most importantly, do not do it without assistance. Go to Nvidia website and ask for help.

Downloads. Index. Contents.

Copyright 2007