I use the 64bit version of CUDA, go here to get it:
http://www.nvidia.com/object/cuda_get.html
(I selected Linux 64 bit and  Ubuntu 9.04 .)

This lets you download the CUDA Drivers, the CUDA Toolkit and the CUDA SDK, and you will end up with three files:

cudadriver_2.3_linux_64_190.18.run

cudatoolkit_2.3_linux_64_ubuntu9.04.run

cudasdk_2.3_linux.run

Installing the NVIDIA driver:

Cuda needs at least version 190 of the linux driver

1.

Uninstall existing NVIDIA drivers and nvidia-glx. (Use gui or aptitude).

2.

Install driver:

Quit x (log out of your desktop, go to a virtual terminal (CTRL+ALT+F1), login, run

$ sudo service gdm stop

3.

Run

$ sudo cudadriver_2.3_linux_64_190.18.run

to install the driver, follow the prompts.
Say yes to install 32 bit compatibility libraries.
If you want you can let the installer overwrite the Xorg configuration file.

4.

Reboot and log back in.

5.

Run

 $ nvidia-settings

to verify that your driver version is at least 190.18. Look for the driver version in the window:

Installing the CUDA Toolkit:

After having installed the driver we now need to install the CUDA toolkit itself.

1.

Run:

$ sudo sh cudatoolkit_2.3_linux_64_ubuntu9.04.run

2.

Press enter to install at the

/usr/local/cuda

default location.

3.

Register the new library files, run:

$ sudo gedit /etc/ld.so.conf.d/cuda.conf

and add

/usr/local/cuda/lib64

to the otherwise empty file and save it. Then run:

$ sudo ldconfig

You can also add

export PATH=/usr/local/cuda/bin:$PATH

export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH

to the end of your ~/.bashrc file.

(You should then login and out again of start a new bash with

bash --

to update your environment.

Installing the CUDA SDK and Compiling the Example Programs

We will now install the CUDA SDK to your own home directory so you can play with the supplied demos:

1.

Run:

$ sh cudasdk_2.3_linux.run

I chose to install it at the default location.

2.

As CUDA does not yet work with GCC 4.4 you will have to  install gcc-4.3:

$ sudo aptitude install gcc-4.3 g++-4.3

3.

Go to SDK source dir:

cd ~/NVIDIA_GPU_Computing_SDK/C$

4.

Create a directory and create symlinks to gcc-4.3/g++-4.3

$ mkdir mygcc

$ cd mygcc

$ ln -s $(which g++-4.3) g++

$ ln -s $(which gcc-4.3) gcc

$ cd ..

5.

Edit common makefile:

$ gedit common/common.mk

Find the lines that specify CC, CXX and LINK and change them to:

CXX        := g++-4.3

CC         := gcc-4.3

LINK       := g++-4.3 -fPIC

Add

#use gcc-4.3

NVCCFLAGS+=--compiler-bindir=${HOME}/NVIDIA_GPU_Computing_SDK/C/mygcc

Before the line that says “ifeq ($(nvcc_warn_verbose),1)”. This tells the nvcc computer that it should look in mygcc for gcc, which will cause it to pickup our gcc-4.3 compiler.

6.

You should now be able to compile everything by running

$ make

This should now compile all the examples in the SDK without errors.

Verify Installation

We can now verify that everything is working:

1.

Run:

$ bin/linux/release/deviceQuery

On my machine I get the following output (depending on your harware, you ourput may be different):

CUDA Device Query (Runtime API) version (CUDART static linking)

There is 1 device supporting CUDA

Device 0: "GeForce 8800 Ultra"
CUDA Driver Version:                           2.30
CUDA Runtime Version:                          2.30
CUDA Capability Major revision number:         1
CUDA Capability Minor revision number:         0
Total amount of global memory:                 804585472 bytes
Number of multiprocessors:                     16
Number of cores:                               128
Total amount of constant memory:               65536 bytes
Total amount of shared memory per block:       16384 bytes
Total number of registers available per block: 8192
Warp size:                                     32
Maximum number of threads per block:           512
Maximum sizes of each dimension of a block:    512 x 512 x 64
Maximum sizes of each dimension of a grid:     65535 x 65535 x 1
Maximum memory pitch:                          262144 bytes
Texture alignment:                             256 bytes
Clock rate:                                    1.51 GHz
Concurrent copy and execution:                 No
Run time limit on kernels:                     Yes
Integrated:                                    No
Support host page-locked memory mapping:       No
Compute mode:                                  Default (multiple host threads can use this device simultaneously)

Test PASSED

Press ENTER to exit...

You should now be good to go. Here’s an screenshot of the volumeRender demo:

$ bin/linux/release/volumeRender

Remember to use gcc-4.3/g++-4.3 and the –compiler-bindir option to nvcc when compiling your own CUDA source.

Yesterday I went shopping in the city with Mette and David from my dorm. I ended up buying some wodden mind puzzle, the objective of which is to assemble a star from several parts, a handgrip gel for strengthening of the wrist and fingers and Pandora’s Star which is the first book in a series written by Peter F. Hamilton, the author of the brilliant Nights Dawn trilogy.

I recently released vplayer which is a xine-lib front-end I wrote at CAVI. The frontend is able to play a movie frame-synchronized on a cluster of computers. It uses SDL to display the video but it is also able to render on an OpenGL texture for use in a graphics engine.

At CAVI I am currently investigating how to best go about constructing a videoplayer running on our cluster. It should also scale to another cluster we are still only designed which will consist of about 7 nodes. The video should be tiled and player in synch on all nodes with one (extra) node handling the audio. I have looked a bit into the source of MPlayer, Xine and videolan (vlc). Videolan allows for the streaming of the video to all nodes and has plugins for dividing the movie into tiles for a walled display. Unfortunately I have not been able to get an multicast IP address from the system administrators yet, so I have only tested it the multiple unicast streams where the nodes weren’t entirely free of synchronization glitches. None of the players seem to have built-in support for stereo rendering in OpenGL, so I will need to update their code anyways. With MPlayer or Xine I will even need to write new plugins, and fiddle with their code to enable them to use a common clock.