Will Ware's blog: CUDA

Showing posts with label CUDA. Show all posts

Wednesday, February 24, 2010

Fixing a versioning problem with CUDA 2.3

In an earlier posting, I observed that CUDA 2.3 wants to use GCC 4.3, which is a problem for Fedora 11 and Ubuntu 9.10. I've been itching to upgrade my distribution on my NVIDIA Linux box, and particularly itching to move to Ubuntu. I found some help on Thomas Moelhave's blog. Thanks, Thomas!

In addition to his instructions, I needed to install some stuff.

sudo aptitude install freeglut3 \
   freeglut3-dev libXmu-dev libXi-dev

Once I did that and completed his instructions, everything worked great. The rest of my Ubuntu 9.10 installation is completely intact and happy.

Monday, October 12, 2009

Hacking CUDA and OpenCL on Fedora 10

I discovered Fedora 11 is not compatible with NVIDIA's CUDA toolkit (now on version 2.3; see note about driver version below) because the latter requires GCC 4.3 where Fedora 11 provides GCC 4.4. So I'll have to back down to Fedora 10. Here are some handy notes for setting up Fedora 10. I installed a number of RPMs to get CUDA to build.

sudo yum install eclipse-jdt eclipse-cdt \
freeglut freeglut-devel kernel-devel \
mesa-libGLU-devel libXmu-devel libXi-devel

The Eclipse stuff wasn't all necessary for CUDA but I wanted it.

In a comment to an earlier posting, Jesper told me about OpenCL, a framework for writing programs that execute across heterogeneous platforms consisting of CPUs, GPUs, and other processors. NVIDIA supports this and has an OpenCL implementation which required updating my NVIDIA drivers to version 190.29, more recent than the version 190.18 drivers on NVIDIA's CUDA 2.3 page. When I installed 190.29, it warned me that it was uninstalling the 190.18 drivers.

Python enthusiasts will be interested in PyOpenCL.

NVIDIA provides a lot of resources and literature for getting started with OpenCL.

Tuesday, July 21, 2009

Building a GPU machine

I've been reading lately about what NVIDIA has been doing with CUDA and it's quite impressive. CUDA is a programming environment for their GPU boards, available for Windows, Linux, and Mac. I am putting together a Linux box with an NVIDIA 9600GT board to play with this stuff. The NVIDIA board cost me $150 at Staples. Eventually I intend to replace it with a GTX280 or GTX285, which both have 240 processor cores to the 9600GT's 64. I purchased the following from Magic Micro, which was about $300 including shipping:

Intel Barebones #2
* Intel Pentium Dual Core E2220 2.4 GHz, 800FSB (Dual Core) 1024K
* Spire Socket 775 Intel fan
* ASRock 4Core1600, G31, 1600FSB, Onboard Video, PCI Express, Sound, LAN
* 4GB (2x2GB) PC6400 DDR2 800 Dual Channel
* AC 97 3D Full Duplex sound card (onboard)
* Ethernet network adapter (onboard)
* Nikao Black Neon ATX Case w/side window & front USB
* Okia 550W ATX Power Supply w/ 6pin PCI-E

I scavenged an old DVD-ROM drive and a 120-gig HD from an old machine, plus a keyboard, mouse, and 1024x768 LCD monitor. I installed Slackware Linux. I went to the CUDA download website and picked up the driver, the toolkit, the SDK, and the debugger.

This is the most powerful PC I've ever put together, and it was a total investment of just a few hundred dollars. For many years I've drooled at the prospect of networking a number of Linux boxes and using them for scientific computation, but now I can do it all in one box. It's a real live supercomputer sitting on my table, and it's affordable.

I am really starting to like NVIDIA. They provide a lot of support for scientific computation. They are very good about sharing their knowledge. They post lots of videos of scientific uses for their hardware.

NVIDIA's SDK includes several demos, some of them visually attractive: n-body, smoke particles, a Julia set, and a fluid dynamics demo. When running the n-body demo, the 9600GT claims to be going at 125 gigaflops.

A few more resources...