- INSTALL CUDA TOOLKIT UBUNTU APT GE HOW TO
- INSTALL CUDA TOOLKIT UBUNTU APT GE INSTALL
- INSTALL CUDA TOOLKIT UBUNTU APT GE DRIVERS
- INSTALL CUDA TOOLKIT UBUNTU APT GE DRIVER
- INSTALL CUDA TOOLKIT UBUNTU APT GE CODE
The output on my system is as follows: - Starting.Īs before, the final line is the most important.
We also want to check the bandwidth to our GPU. It states that the test was successful as we received a "PASS".
INSTALL CUDA TOOLKIT UBUNTU APT GE DRIVER
> Peer access from GeForce GTX 780 Ti (GPU1) -> GeForce GTX 780 Ti (GPU0) : YesĭeviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 6.5, CUDA Runtime Version = 6.5, NumDevs = 2, Device0 = GeForce GTX 780 Ti, Device1 = GeForce GTX 780 Ti > Peer access from GeForce GTX 780 Ti (GPU0) -> GeForce GTX 780 Ti (GPU1) : Yes Support host page-locked memory mapping: Yesĭevice supports Unified Addressing (UVA): Yesĭevice PCI Bus ID / PCI location ID: 1 / 0 Max dimension size of a grid size (x,y,z): (2147483647, 65535, 65535)Ĭoncurrent copy and kernel execution: Yes with 1 copy engine(s) Max dimension size of a thread block (x,y,z): (1024, 1024, 64) Maximum number of threads per block: 1024 Maximum number of threads per multiprocessor: 2048 Total number of registers available per block: 65536 Total amount of shared memory per block: 49152 bytes Total amount of constant memory: 65536 bytes Maximum Layered 1D Texture Size, (num) layers 1D=(16384), 2048 layers (15) Multiprocessors, (192) CUDA Cores/MP: 2880 CUDA Cores Total amount of global memory: 3072 MBytes (3220897792 bytes) deviceQuery Starting.ĬUDA Device Query (Runtime API) version (CUDART static linking)ĬUDA Driver Version / Runtime Version 6.5 / 6.5ĬUDA Capability Major/Minor version number: 3.5 I have two GPU cards in SLI configuration on my system and so I've only shown the output for the first device. Once complete we can run the deviceQuery script to test if we can communicate with the GPU: cd bin/x86_64/linux/release
INSTALL CUDA TOOLKIT UBUNTU APT GE CODE
In the following code sample below, change to your preferred installation location for the sample scripts: cuda-install-samples-6.5.sh Ĭhange directory to the /NVIDIA_CUDA-6.5_Samples and run the make command: cd /NVIDIA_CUDA-6.5_Samples In order to check that the installation was successful we are going to compile the CUDA samples, test that we can query the GPU device and ascertain its bandwidth. The output on my system is as follows nvcc: NVIDIA (R) Cuda compiler driverĬopyright (c) 2005-2014 NVIDIA CorporationĬuda compilation tools, release 6.5, V6.5.12 GCC version: gcc version 4.8.2 (Ubuntu 4.8.2-19ubuntu1)Ĭheck the version of the Nvidia CUDA compiler: nvcc -V The output on my system is as follows NVRM version: NVIDIA UNIX x86_64 Kernel Module 331.89 Tue Jul 1 13:30: The following line will provide us with the driver version: cat /proc/driver/nvidia/version
INSTALL CUDA TOOLKIT UBUNTU APT GE DRIVERS
Remember to make sure that the terminal has access to these variables: source ~/.bash_profileīefore proceeding to test the GPU cards we will ensure that the drivers are correctly installed.
bash_profile file in our home directory, in order to obtain the required compilation tools on our PATH: export PATH=/usr/local/cuda-6.5/bin:$PATHĮxport LD_LIBRARY_PATH=/usr/local/cuda-6.5/lib64:$LD_LIBRARY_PATH We also need to add the following lines to our.
INSTALL CUDA TOOLKIT UBUNTU APT GE INSTALL
The following commands will install CUDA 6.5: sudo dpkg -i cuda-repo-ubuntu1404_6.5-14_b I placed this in my home Downloads directory: cd ~/Downloads The next step is to download the specific DEB package for the 64-bit version of CUDA for Ubuntu 14.04. I'll assume that you have a 64-bit system for the remainder of the article.
This is carried out by installing the build-essential package: sudo apt-get install build-essential The first task is to make sure that you have the GNU compiler collection (GCC) tools installed. In this article I am going to describe the same procedure but carry it out under the latest version of Ubuntu, namely 14.04.
INSTALL CUDA TOOLKIT UBUNTU APT GE HOW TO
In a previous article Valerio Restocchi showed us how to install Nvidia CUDA on a Mac OS X system. CUDA is the industry standard for working with GPU-HPC. In this article I am going to discuss how to install the Nvidia CUDA toolkit for carrying out high-performance computing (HPC) with an Nvidia Graphics Processing Unit (GPU).
0 kommentar(er)
