Quantitative Analysis
Parallel Processing
Numerical Analysis
C++ Multithreading
Python for Excel
Python Utilities
Services
Author
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.
Introduction into GPU programming.
his section is an introduction into GPU and Cuda. It is not a substitution for original documentation but only a suggestion of one way to become familiar with the technology.
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.
Downloads.
Index.
Contents.
Copyright 2007
