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Here's a basic guide for getting CUDA 7.0 and the R package gputools running perfectly under Ubuntu 14.10. It's not difficult, but there are a few issues and this will be helpful to have in a single place.
If you're running Ubuntu 14.10, I'd recommend installing CUDA 7.0. NVIDIA has a 7.0 Debian package specifically for 14.10; this wasn't the case for CUDA 6.5, which only had a Debian package for 14.04.
To get access to CUDA 7.0, you'll first need to register as a CUDA developer.
Join The CUDA Registered Developer Program
Once you have access, navigate to the CUDA 7.0 download page and get the Debian package.
CUDA 7.0 Release Candidate Downloads
You'll either need to be running the NVIDIA 340 or 346 drivers. If you're having trouble upgrading, I'd suggest adding the xorg-edgers PPA.
Once your NVIDIA driver is set, install the CUDA 7.0 Debian package you've downloaded. Don't forget to remove any previously installed CUDA packages or repositories.
You'll need to add paths so everything knows where CUDA is installed. Append the following to the .bashrc in your home directory:
Execute "source ~/.bashrc" for these changes to be applied. If you want to test your new CUDA install, make the samples provided by NVIDIA.
I get the following output when running BlackScholes:
The next task is to install gputools for R. You can't unfortunately install the current package through R, as the source code contains references to CUDA architectures that are obsolete under CUDA 7.0. But that's easy to fix.
Now do some editing in gputools/src/Makefile:
Now build and install the patched gputools package while you're in the directory immediately above gputools:
If you want to make the gputools packages available for all R users
Keep in mind that they'll have to make the same environmental variable changes as above. Let's test it!
Running gives us:
A nice 26-fold speedup. We're all set!
If you're running Ubuntu 14.10, I'd recommend installing CUDA 7.0. NVIDIA has a 7.0 Debian package specifically for 14.10; this wasn't the case for CUDA 6.5, which only had a Debian package for 14.04.
To get access to CUDA 7.0, you'll first need to register as a CUDA developer.
Join The CUDA Registered Developer Program
Once you have access, navigate to the CUDA 7.0 download page and get the Debian package.
CUDA 7.0 Release Candidate Downloads
You'll either need to be running the NVIDIA 340 or 346 drivers. If you're having trouble upgrading, I'd suggest adding the xorg-edgers PPA.
Once your NVIDIA driver is set, install the CUDA 7.0 Debian package you've downloaded. Don't forget to remove any previously installed CUDA packages or repositories.
You'll need to add paths so everything knows where CUDA is installed. Append the following to the .bashrc in your home directory:
Execute "source ~/.bashrc" for these changes to be applied. If you want to test your new CUDA install, make the samples provided by NVIDIA.
I get the following output when running BlackScholes:
The next task is to install gputools for R. You can't unfortunately install the current package through R, as the source code contains references to CUDA architectures that are obsolete under CUDA 7.0. But that's easy to fix.
Now do some editing in gputools/src/Makefile:
Now build and install the patched gputools package while you're in the directory immediately above gputools:
If you want to make the gputools packages available for all R users
Keep in mind that they'll have to make the same environmental variable changes as above. Let's test it!
Running gives us:
A nice 26-fold speedup. We're all set!
thanks - got it working with the -arch change and a few other hacks
ReplyDeleteHmm, I can't get it to work. GPUTools is up to version 0.5, and when I follow these steps I get this error:
ReplyDelete** testing if installed package can be loaded
Error in library.dynam(lib, package, package.lib) :
shared object ‘gputools.so’ not found
Error: loading failed
Execution halted
ERROR: loading failed
* removing ‘/home/boris/R/x86_64-pc-linux-gnu-library/3.0/gputools’
I also couldn't get these steps to work with gputools version 0.5 and CUDA-7.5
DeleteDid you ever find a solution?