Ubuntu16.04 + cuda8.0 + GTX1080 Installation Tutorial

1. Installing Ubuntu16.04

Regardless of dual system, install Ubuntu16.04 directly from Ubuntu official download 64-bit version: Ubuntu-16.04-desktop-amd64.iso.

The Ubuntu USB installation disk is made under Mac, which can be referenced by using ISO to make Linux installation USB disk under Mac, then booting the Ubuntu system via BIOS boot USB drive:

1) First install on a hole, choose "Install Ubuntu" after a while the screen display "input does not support", Google has a lot of programs, and finally and Ubuntu support for graphics card, need to manually add graphics options: Nomodeset, To enable it to support Nvidia series graphics, refer to: Install Ubuntu black screen problem resolution or how does I set ' nomodeset ' after I ' ve already installed Ubuntu?

2) disk partition, all kill before the host Windows 10 system, partition for/boot,/,/home and several other directories, while the second block of 4T hard drive also mounted up, as a data disk.

3) After installation, the Ubuntu 16.04 resolution is very low, you can manually modify the grub file before the graphics driver is installed:

sudo vim/etc/default/grub

# The resolution used on graphical terminal
# Note that you can use only modes which your graphic card supports via VBE
# you can see them in real GRUB with the command ' Vbeinfo '
#GRUB_GFXMODE =640x480
# Here the resolution is set by itself
grub_gfxmode=1024x768

sudo update-grub

4) Install SSH Server so that the GTX1080 host can be accessed remotely by SSH:

sudo apt-get install Openssh-server

5) Update the Ubuntu16.04 source, using the source of the Zhong Ke:

cd/etc/apt/
sudo cp sources.list Sources.list.bak
sudo vi sources.list

Add the following sources to the header of the Source.list file:

Deb http://mirrors.ustc.edu.cn/ubuntu/xenial main restricted universe multiverse 
Deb/http Mirrors.ustc.edu.cn/ubuntu/xenial-security main restricted universe multiverse 
Deb http:/ Mirrors.ustc.edu.cn/ubuntu/xenial-updates main restricted universe multiverse 
Deb http:/ mirrors.ustc.edu.cn/ubuntu/xenial-proposed main restricted universe multiverse 
Deb http:/ Mirrors.ustc.edu.cn/ubuntu/xenial-backports main restricted universe multiverse 
Deb-src/HTTP/ Mirrors.ustc.edu.cn/ubuntu/xenial main restricted universe multiverse 
Deb-src http://mirrors.ustc.edu.cn/ Ubuntu/xenial-security main restricted universe multiverse 
Deb-src http://mirrors.ustc.edu.cn/ubuntu/ Xenial-updates main restricted universe multiverse 
Deb-src http://mirrors.ustc.edu.cn/ubuntu/ xenial-proposed main restricted universe multiverse 
Deb-src http://mirrors.ustc.edu.cn/ubuntu/ Xenial-backports main restricted universe multiverse

Finally update the source and update the installed packages:

sudo apt-get update
sudo apt-get upgrade

2. installing the GTX1080 drive

Installing the Nvidia driver 367.27

sudo add-apt-repository Ppa:graphics-drivers/ppa

The first run appears with the following warning:

Fresh drivers from upstream, currently shipping Nvidia.

# # Current Status

We currently recommend:NVIDIA−361nvidia-361, Nvidia ' s current long lived branch.
For GeForce 8 and 9 Series GPUs useNVIDIA−340nvidia-340
For GeForce 6 and 7 Series GPUs usenvi d i A− 304 nvidia-304

# What do we ' re working on right now:

–normal driver updates–investigating How to bring the goodness to distro on a cadence.

# # WARNINGS:

This PPA are currently in testing, and you should be experienced with packaging before you dive on here. Give us a few days to sort out the kinks.

Volunteers welcome! See also:https://github.com/mamarley/nvidia-graphics-drivers/

Http://www.ubuntu.com/download/desktop/contribute
More information: Https://launchpad.net/~graphics-drivers/+archive/ubuntu/ppa
Press ENTER to continue or CTRL + C to cancel the add

Continue after carriage return:

sudo apt-get update
sudo apt-get install nvidia-367
sudo apt-get install Mesa-common-dev
sudo apt-get install Freeglut3-dev

Then reboot the system for the GTX1080 graphics driver to take effect.

3. Download and install Cuda

Before installing CUDA, Google a bit, found in Ubuntu16.04 installed CUDA7.5 problems, fortunately CUDA8 has been out, support GTX1080:

New in CUDA 8

Pascal Architecture Support
Out of box performance improvements on Tesla P100, supports GeForce GTX 1080
Simplify programming using Unified memory on Pascal including support for large datasets, concurrent data access and Atomi Ash
Optimize Unified Memory Performance using new data migration apis*
Faster deep Learning using optimized cublas routines for native FP16 computation
Developer Tools
Quickly identify latent System-level bottlenecks using the new critical Path analysis feature
Improve productivity with up to 2x faster NVCC compilation speed
Tune OPENACC applications and overall host code using new profiling extensions
Libraries
Accelerate graph analytics algorithms with Nvgraph
New Cublas matrix multiply optimizations for matrices with sizes smaller than + and for batched operation

However, download Cuda needs to register and login to NVIDIA developer account, CUDA8 download page provides detailed system selection and installation instructions,

Here choose the Ubuntu16.04 system runfile installation scheme, do not choose the Deb scheme, the front of countless pits:

Download "Cuda_8.0.27_linux.run" has 1.4G, according to Nivdia official method to install CUDA8:

sudo sh cuda_8.0.27_linux.run--tmpdir=/opt/temp/

Here add –tmpdir mainly directly run "sudo sh cuda_8.0.27_linux.run" will prompt the lack of space error, in fact, is a brand-new computer mainframe, hard disk large enough, Google found the following add a tmpdir can be:

Not enough space on parition mounted at/. Need 5091561472 bytes.

Disk space Check has failed. Installation cannot continue.

After execution there will be a series of prompts to confirm that it is very, very, very critical to install the 361 low version of the driver:

Install NVIDIA accelerated Graphics Driver for linux-x86_64 361.62?

The answer must be n, otherwise the previously installed GTX1080 drivers are wasted and there are a lot of problems.

Logging To/opt/temp//cuda_install_6583.log
Using the more to view the EULA.
End User License Agreement
————————–

Preface ——-

The following contains specific license terms and conditions
For four separate NVIDIA products. By accepting this
Agreement, agree to comply with all the terms and
Conditions applicable to the specific product (s) included
Herein.

Do you accept the previously read eula?accept/decline/quit:accept

Install NVIDIA accelerated Graphics Driver for linux-x86_64 361.62? (y) es/(n) o/(q) uit:n

Install the CUDA 8.0 Toolkit? (y) es/(n) o/(q) uit:y

Enter Toolkit location[default is/usr/local/cuda-8.0]:

Do you want to install a symbolic link At/usr/local/cuda? (y) es/(n) o/(q) uit:y

Install the CUDA 8.0 Samples? (y) es/(n) o/(q) uit:y

Enter CUDA Samples location[default Is/home/textminer]:

Installing the CUDA Toolkit in/usr/local/cuda-8.0 ...
Installing the CUDA Samples in/home/textminer ...
Copying Samples To/home/textminer/nvidia_cuda-8.0_samples Now ...
Finished copying samples.

===========
= Summary =
===========

Driver:not Selected
Toolkit:installed in/usr/local/cuda-8.0
Samples:installed In/home/textminer

sure that
–path Includes/usr/local/cuda-8.0/bin
–ld_library_path includes/usr/local/cuda-8.0/lib64, or, add/usr/local/cuda-8.0/lib64 to/etc/ld.so.conf and run ldconf IG as Root

To uninstall the CUDA Toolkit, run the uninstall script In/usr/local/cuda-8.0/bin

Please see Cuda_installation_guide_linux.pdf in/usr/local/cuda-8.0/doc/pdf for detailed information on setting up CUDA.

Warning:incomplete installation! This installation does not install the CUDA Driver. A driver of version at least 361.00 are required for CUDA 8.0 functionality to work.
To install the driver using this installer, run the following command, replacing with the name of this run file:
sudo. run-silent-driver

Logfile Is/opt/temp//cuda_install_6583.log

Once the installation is complete, declare the environment variable and write it to the tail of the ~/.BASHRC:

Export Path=/usr/local/cuda-8.0/bin${path:+:${path}}export ld_library_path=/usr/local/cuda-8.0/lib64${ld_library _path:+:${ld_library_path}}

Finally, let's Test Cuda and run:

Nvidia-smi

The results are as follows:

Try some Cuda examples:

CD 1_utilities/devicequerymake

If you are prompted that the GCC version is too high, you can install the lower version of GCC and do a soft connection replacement, the specific method please Google, I am using the lower version of the gcc4.9 replaced the gcc5.x version of ubuntu16.04.

"/usr/local/cuda-8.0″/bin/nvcc-ccbin g++-I.. /.. /common/inc-m64-gencode Arch=compute_20,code=sm_20-gencode Arch=compute_30,code=sm_30-gencode arch=compute_35, Code=sm_35-gencode Arch=compute_37,code=sm_37-gencode Arch=compute_50,code=sm_50-gencode Arch=compute_52,code=sm_ 52-gencode Arch=compute_60,code=sm_60-gencode arch=compute_60,code=compute_60-o devicequery.o-c deviceQuery.cpp
"/usr/local/cuda-8.0″/bin/nvcc-ccbin g++-m64-gencode arch=compute_20,code=sm_20-gencode arch=compute_30,code=sm_ 30-gencode Arch=compute_35,code=sm_35-gencode Arch=compute_37,code=sm_37-gencode arch=compute_50,code=sm_50- Gencode Arch=compute_52,code=sm_52-gencode Arch=compute_60,code=sm_60-gencode Arch=compute_60,code=compute_60-o Devicequery DEVICEQUERY.O
Mkdir-p. /.. /bin/x86_64/linux/release
CP Devicequery. /.. /bin/x86_64/linux/release

Execute./devicequery, get:

./devicequery starting ...

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

Detected 1 CUDA capable device (s)

Device 0: "GeForce GTX 1080"
CUDA Driver version/runtime Version 8.0/8.0
CUDA Capability Major/minor Version number:6.1
Total amount of global memory:8112 MBytes (8506179584 bytes)
(+) Multiprocessors, (+) Cuda cores/mp:2560 cuda cores
GPU Max Clock rate:1835 MHz (1.84 GHz)
Memory Clock rate:5005 Mhz
Memory Bus Width:256-bit
L2 Cache size:2097152 bytes
Maximum Texture Dimension Size (x, Y, z) 1d= (131072), 2d= (131072, 65536), 3d= (16384, 16384, 16384)
Maximum layered 1D Texture Size, (num) layers 1d= (32768), 2048 layers
Maximum layered 2D Texture Size, (num) layers 2d= (32768, 32768), 2048 layers
Total amount of constant memory:65536 bytes
Total amount of shared memory per block:49152 bytes
Total number of registers available per block:65536
Warp size:32
Maximum number of threads per multiprocessor:2048
Maximum number of threads per block:1024
Max dimension size of a thread block (x, Y, z): (1024, 1024, 64)
Max dimension size of a grid size (x, Y, z): (2147483647, 65535, 65535)
Maximum Memory pitch:2147483647 bytes
Texture alignment:512 bytes
Concurrent copy and kernel Execution:yes with 2 copy engine (s)
Run time limit on Kernels:yes
Integrated GPU sharing Host Memory:no
Support Host page-locked Memory Mapping:yes
Alignment requirement for Surfaces:yes
Device has ECC support:disabled
Device supports Unified Addressing (UVA): Yes
Device PCI Domain id/bus id/location id:0/1/0
Compute Mode:
<default (Multiple host threads can use:: Cudasetdevice () with device simultaneously) >

Devicequery, Cuda Driver = Cudart, cuda Driver version = 8.0, cuda Runtime Version = 8.0, Numdevs = 1, Device0 = GeForce G TX 1080Result = PASS

Try the test again nobody:

Cd.. /.. /5_simulations/nbody/make

Perform:

./nbody-benchmark-numbodies=256000-device=0

Get:

> Windowed Mode
> Simulation data stored in video memory
> Single precision floating point simulation
> 1 Devices used for simulation
Gpudeviceinit () CUDA Device [0]: "GeForce GTX 1080
> Compute 6.1 CUDA device: [GeForce GTX 1080]
Number of bodies = 256000
256000 bodies, total time for ten iterations:2291.469 ms
= 286.000 billion interactions per second
= 5719.998 single-precision gflop/s at flops per interaction

Reference:
Nvidia GTX in Ubuntu 16.04 for deep learning
Ubuntu 16.04 under Install TensorFlow (GPU)
ubuntu16.04 installation cuda7.5
Ubuntu16.04 Unable to install Cuda?
Ubuntu16.04+matlab2014a+anaconda2+opencv3.1+caffe Installation
Ubuntu 16.04 compilation OPENCV3.1,OPENCV multi-version switching
TensorFlow, Caffe, Chainerとdeep learning Big Imperial a Genki にsource code BUILDでGPU to けにsetupしてみた
Feature Request:support for Cuda 8.0 RC
GTX CUDA performance on Linux (Ubuntu 16.04) Preliminary results (Nbody and NAMD)
Anyone able to run TensorFlow with 1070/1080 on Ubuntu 16.04/15.10/15.04?
TensorFlow on Ubuntu 16.04 with Nvidia GTX 1080

Ubuntu16.04 + cuda8.0 + GTX1080 Installation Tutorial