Tekst źródłowy strony Programowanie w systemie UNIX/CUDA

[[File:Cuda1.png|Porównanie wydajności CPU i GPU|ramka]]


{{Info|Książka wymaga poprawek: nie można zainstalować CUDA !!!}}



Wstęp do CUDA <ref>[http://www.knsi.pwr.wroc.pl/cuda/?p=125 Wstęp do CUDA cz. I autor: Szymon Zimnowoda]</ref>
==Instalacja==

Opis<ref>[https://docs.nvidia.com/cuda/cuda-quick-start-guide/index.html docs.nvidia : cuda-quick-start-guide]</ref><ref>[http://askubuntu.com/questions/131506/how-can-i-get-nvidia-cuda-or-opencl-working-on-a-laptop-with-nvidia-discrete-car|opis instalacji : ask ubuntu]</ref><ref>[http://www.thomas-krenn.com/pl/wiki/Instalacja_CUDA | Instalacja CUDA Thomas Krenn Wiki]</ref>
<ref>[http://help.ubuntu.com/community/Cuda Cuda HELP UBUNTU]</ref><ref>[https://tutorials.ubuntu.com/tutorial/gpu-data-processing-inside-lxd tutorials.ubuntu : gpu-data-processing-inside-lxd]</ref>
* [https://developer.nvidia.com/cuda-downloads?target_os=Linux&target_arch=x86_64&Distribution=Ubuntu&target_version=22.04&target_type=runfile_local CUDA toolkit 12.2] 

 sudo dpkg --install cuda-repo-<distro>-<version>.<architecture>.deb
 sudo apt-key del 7fa2af80
 wget https://developer.download.nvidia.com/compute/cuda/repos/$distro/$arch/cuda-keyring_1.0-1_all.deb
 sudo dpkg -i cuda-keyring_1.0-1_all.deb
 sudo add-apt-repository contrib
 sudo apt-get update
 sudo apt-get -y install cuda
 sudo reboot
 export PATH=/usr/local/cuda-12.2/bin${PATH:+:${PATH}}
 export LD_LIBRARY_PATH=/usr/local/cuda-12.2/lib64 ${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
 git clone git@github.com:NVIDIA/cuda-samples.git
 
 
Instalacja
* Docker<ref>[https://linuxhandbook.com/setup-opencl-linux-docker/ linuxhandbook : setup-opencl-linux-docker]</ref>
===sprzęt===
Sprawdzamy jaką mamy kartę graficzną:<ref>[http://docs.nvidia.com/cuda/cuda-getting-started-guide-for-linux/  cuda-getting-started-guide-for-linux]</ref> 

 lspci | grep -i nvidia

lub za pomocą komendy: 

 lspci | grep VGA
 lshw -c video | grep driver
 ubuntu-drivers devices


lub<ref>[https://linuxconfig.org/how-to-check-cuda-version-on-ubuntu-20-04-focal-fossa-linux linuxconfig: how-to-check-cuda-version-on-ubuntu-20-04-focal-fossa-linux]</ref>

  nvidia-smi 


i czy jest ona na liście kompatybilnego sprzętu: <ref>[https://developer.nvidia.com/cuda-gpus Lista GPU odpowiednich dla CUDA]</ref>

 https://developer.nvidia.com/cuda-gpus

* [https://developer.nvidia.com/cuda-gpus GeForce GTX 770	: Compute Capability = 3.0]

====Nvidia GTX 770 ====

 The GeForce GTX 770 is a graphics card featuring the next-gen Kepler-based GPU with 1536 CUDA cores, It features an amazing GDDR5 memory at 7GHz. It sports a pair of SLI connectors, 6-pin and 8-pin PCIe power connectors, and an I/O panel consisting of HDMI, DisplayPort and two DVI ports. The GTX 770 packs 4 graphics processing clusters, 8 streaming multiprocessors, 128 TAUs and 32 ROPs.<ref>[https://www.techspot.com/products/graphics-cards/nvidia-geforce-gtx-770.91181/ techspot : products nvidia-geforce-gtx-770.91181]</ref>

Specyfikacja: 
* GTX 770 GPU Engine Specs:
  1536CUDA Cores
  1046Base Clock (MHz)
  1085Boost Clock (MHz)
  134Texture Fill Rate (billion/sec)
GTX 770 Memory Specs:
  7.0 GbpsMemory Speed
  2048 MBStandard Memory Config
  GDDR5Memory Interface
  256-bitMemory Interface Width
  224.3Memory Bandwidth (GB/sec)
GTX 770 Support:
  GPU Boost 2.0, PhysX, TXAA, SHIELD-Ready, NVIDIA G-SYNC-readyImportant Technologies
  3D Vision, CUDA, DirectX 11, Adaptive VSync, FXAA,
  NVIDIA Surround, SLI-readyOther Supported Technologies
  4.3OpenGL
  PCI Express 3.0Bus Support
  YesCertified for Windows 7, Windows 8, Windows Vista, or Windows XP
  Yes3D Vision Ready
  Yes3D Gaming
  YesBlu Ray 3D
  Yes3D Vision Live (Photos and Videos)
  YesNVIDIA PhysX™ Technology
  11Microsoft DirectX 11.2 API
Display Support:
  4096x2160Maximum Digital Resolution 1
  2048x1536Maximum VGA Resolution
  One Dual Link DVI-I, One Dual Link DVI-D, One HDMI, One DisplayPortStandard Display Connectors
  4 displaysMulti Monitor
  YesHDCP
  YesHDMI
  InternalAudio Input for HDMI
GTX 770 Graphics Card Dimensions:
  4.376 inchesHeight
  10.5 inchesLength
  Dual-slotWidth
Thermal and Power Specs:
  98 CMaximum GPU Temperature (in C)
  230 WGraphics Card Power (W)
  600 WMinimum Recommended System Power (W)
  One 8-pin and one 6-pinSupplementary Power Connectors
1 - 3840x2160 at 30Hz or 4096x2160 at 24Hz supported over HDMI. 4096x2160 (including 3840x2160) at 60Hz supported over Displayport. Support for 4k tiled MST displays requires 326.19 driver or later.


Graphics Features<ref>[https://www.techpowerup.com/gpu-specs/geforce-gtx-770.c1856 techpowerup : gpu-specs geforce-gtx-770.c1856]</ref>
* DirectX 12 (11_0)
* OpenGL 4.6
* OpenCL 3.0
* Vulkan 1.2.175
* CUDA 3.0
* Shader Model 6.5 (5.1)

Render Config
* Shading Units 1536
* TMUs 128 ( texture mapping units)
* ROPs 32
* SMX Count 8  ( The Next Generation Streaming Multiprocessor SMX) 
* L1 Cache 16 KB (per SMX)
* L2 Cache 512 KB

Graphics Processor
* GPU Name GK104
*  GPU Variant GK104-425-A2
* Architecture Kepler
* Foundry TSMC
* Process Size 28 nm
* Transistors 3,540 million
* Density 12.0M / mm²
* Die Size 294 mm²
* Chip Package BGA-1745
* the SMX now contains 196 cores 
* 8 SMX * 196 cores = 1536 cores to reach 3,250 Gflops


Theoretical Performance
* Pixel Rate 34.72 GPixel/s
* Texture Rate 138.9 GTexel/s
* FP32 (float) 3.333 TFLOPS
* FP64 (double) 138.9 GFLOPS (1:24)

===OS===
Wybieramy taką wersję OS dla której jest dostępny  CUDA Toolkit<ref>[https://developer.nvidia.com/cuda-downloads CUDA Toolkit]</ref>


Sprawdzamy aktualną wersję i typ OS:

 uname -m && cat /etc/*release

otrzymujemy (przykładowy wynik):

<pre>
x86_64
DISTRIB_ID=Ubuntu
DISTRIB_RELEASE=13.10
DISTRIB_CODENAME=saucy
DISTRIB_DESCRIPTION="Ubuntu 13.10"
NAME="Ubuntu"
VERSION="13.10, Saucy Salamander"
ID=ubuntu
ID_LIKE=debian
PRETTY_NAME="Ubuntu 13.10"
VERSION_ID="13.10"
HOME_URL="http://www.ubuntu.com/"
SUPPORT_URL="http://help.ubuntu.com/"
BUG_REPORT_URL="http://bugs.launchpad.net/ubuntu/"
</pre>


Informacje o instalacji dla wersji:
* Ubuntu 16.04<ref>[https://askubuntu.com/questions/799184/how-can-i-install-cuda-on-ubuntu-16-04 askubuntu question: how-can-i-install-cuda-on-ubuntu-16-04]</ref><ref>[https://devtalk.nvidia.com/default/topic/995277/cuda-8-0-toolkit-install-nvcc-not-found-ubuntu-16-04/ devtalk.nvidia : cuda-8-0-toolkit-install-nvcc-not-found-ubuntu-16-04]</ref>
* Ubuntu 14.10<ref>[https://developer.nvidia.com/cuda-downloads|CUDA downloads]</ref>
* Ubuntu 14.04 <ref>[http://askubuntu.com/questions/451672/installing-and-testing-cuda-in-ubuntu-14-04 Installing and testing CUDA in Ubuntu 14.04]</ref>
* Ubuntu 12.04<ref>[https://www.youtube.com/watch?v=qYYnTdX6EwI  Yputube : Ubuntu 12.04 - Installing Nvidia CUDA Developer Driver [OUTDATED]]</ref>

=== cuda-drivers===

  apt search cuda-drivers #  This command lists all the available CUDA versions




<pre>
Sorting... Done
Full Text Search... Done
cuda-drivers/unknown 535.86.10-1 amd64
  CUDA Driver meta-package, branch-agnostic

cuda-drivers-515/unknown 515.105.01-1 amd64
  CUDA Driver meta-package, branch-specific

cuda-drivers-520/unknown 520.61.05-1 amd64
  CUDA Driver meta-package, branch-specific

cuda-drivers-525/unknown 525.125.06-1 amd64
  CUDA Driver meta-package, branch-specific

cuda-drivers-530/unknown 530.30.02-1 amd64
  CUDA Driver meta-package, branch-specific

cuda-drivers-535/unknown 535.86.10-1 amd64
  CUDA Driver meta-package, branch-specific

cuda-drivers-fabricmanager/unknown 535.86.10-1 amd64
  Meta-package for FM and Driver

cuda-drivers-fabricmanager-450/jammy-updates,jammy-security 450.248.02-0ubuntu0.22.04.1 amd64
  Meta-package for FM and Driver

cuda-drivers-fabricmanager-460/jammy-updates,jammy-security 510.85.02-0ubuntu0.22.04.1 amd64
  Transitional package for cuda-drivers-fabricmanager-510

cuda-drivers-fabricmanager-470/jammy-updates,jammy-security 470.199.02-0ubuntu0.22.04.1 amd64
  Meta-package for FM and Driver

cuda-drivers-fabricmanager-510/jammy-updates,jammy-security 515.105.01-0ubuntu0.22.04.1 amd64
  Transitional package for cuda-drivers-fabricmanager-515

cuda-drivers-fabricmanager-515/unknown 515.105.01-1 amd64
  Meta-package for FM and Driver

cuda-drivers-fabricmanager-520/unknown 520.61.05-1 amd64
  Meta-package for FM and Driver

cuda-drivers-fabricmanager-525/unknown 525.125.06-1 amd64
  Meta-package for FM and Driver

cuda-drivers-fabricmanager-530/unknown 530.30.02-1 amd64
  Meta-package for FM and Driver

cuda-drivers-fabricmanager-535/unknown 535.86.10-1 amd64
  Meta-package for FM and Driver
</pre>


===Sterownik karty graficznej ===

Sterownik <ref>[http://http.download.nvidia.com/XFree86/Linux-x86/1.0-8776/README/index.html|NVIDIA Accelerated Linux Driver Set README and Installation Guide]</ref>

 sudo software-properties-gtk

lub:

 nvidia-settings

lub

 apt list --installed | grep -e nvidia-driver-[0-9][0-9][0-9] -e nvidia-[0-9][0-9][0-9]



'''Rekomendowany sterownik''': 

  ubuntu-drivers devices


Przykładowy wynik: 
<pre>
== /sys/devices/pci0000:00/0000:00:01.0/0000:01:00.0 ==
modalias : pci:v000010DEd00001184sv00001458sd00003603bc03sc00i00
vendor   : NVIDIA Corporation
model    : GK104 [GeForce GTX 770]
driver   : nvidia-driver-450-server - distro non-free
driver   : nvidia-driver-390 - distro non-free
driver   : nvidia-driver-418-server - distro non-free
driver   : nvidia-driver-470 - distro non-free recommended
driver   : nvidia-driver-470-server - distro non-free
driver   : xserver-xorg-video-nouveau - distro free builtin
</pre>

'''Aktualny sterownik''': 

 nvidia-smi

===gcc===

Czy mamy odpowiednią<ref>[http://docs.nvidia.com/cuda/cuda-installation-guide-linux/#axzz4V08RvY8D nvidia cuda-installation-guide-linux - table]</ref> wersję [[Programowanie_w_systemie_UNIX/gcc|gcc]] ?


<pre>
        _______________________________________________________________
	|           CUDA version          | max supported GCC version |
	|:-------------------------------:|:-------------------------:|
	|            12.1, 12.2           |           12.2            |
	|                12               |           12.1            |
        | 11.4.1+, 11.5, 11.6, 11.7, 11.8 |            11             |
	|     11.1, 11.2, 11.3, 11.4.0    |            10             |
	|                11               |             9             |
	|            10.1, 10.2           |             8             |
	|             9.2, 10.0           |             7             |
	|             9.0, 9.1            |             6             |
	|                8                |            5.3            |
	|                7                |            4.9            |
	|              5.5, 6             |            4.8            |
	|              4.2, 5             |            4.6            |
	|               4.1               |            4.5            |
	|               4.0               |            4.4            |
        ____________________________________________________________
</pre>




Sprawdzamy wersję gcc

 gcc --version

 apt policy gcc

 gcc:
  Installed: 4:11.2.0-1ubuntu1
  Candidate: 4:11.2.0-1ubuntu1
  Version table:
 *** 4:11.2.0-1ubuntu1 500
        500 http://pl.archive.ubuntu.com/ubuntu jammy/main amd64 Packages
        100 /var/lib/dpkg/status






W przeciwnym razie pojawi się błąd w czasie instalacji : 

  Error: unsupported compiler: 5.4.1. Use --override to override this check.


Rozwiązanie<ref>[http://stackoverflow.com/questions/6622454/cuda-incompatible-with-my-gcc-version stackoverflow question : cuda-incompatible-with-my-gcc-version]</ref> : 
* sudo sh cuda_8.0.44_linux.run --override


lina 81:/usr/local/cuda/include/host_config.h 

 //#if __GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ > 4)
 #if __GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ > 6)

Wybieramy odpowiednią wersję gcc za pomocą update-alternatives
 sudo update-alternatives --config gcc



 sudo gedit /var/log/cuda-installer.log 

<pre>
[INFO]: Driver installation detected by command: apt list --installed | grep -e nvidia-driver-[0-9][0-9][0-9] -e nvidia-[0-9][0-9][0-9]
[INFO]: Cleaning up window
[INFO]: Complete
[INFO]: Checking compiler version...
[INFO]: gcc location: /usr/bin/gcc

[INFO]: gcc version: gcc version 10.4.0 (Ubuntu 10.4.0-4ubuntu1~22.04) 

[INFO]: Initializing menu
[INFO]: Setup complete
[INFO]: Components to install: 
[INFO]: Driver
[INFO]: 470.42.01
[INFO]: Executing NVIDIA-Linux-x86_64-470.42.01.run --ui=none --no-questions --accept-license --disable-nouveau --no-cc-version-check --install-libglvnd  2>&1
[INFO]: Finished with code: 256
[ERROR]: Install of driver component failed.
[ERROR]: Install of 470.42.01 failed, quitting
</pre>

=== SDK =  NVIDIA CUDA Toolkit===
Nvidia CUDA toolkit<ref>[https://developer.nvidia.com/cuda-toolkit nvidia cuda-toolkit]</ref>

  dpkg -l | grep cuda-toolkit

lub

  nvcc --version


Instalacja
* apt
* instaler runfile

Instalacja:
* na pulpicie naciśnij ALT+F1, spowoduje to przejście do ekranu konsoli
* zaloguj się jako root( tylko root może instalować moduły jądra)
* sudo systemctl isolate multi-user.target    
* run the nVidia install script
* reboot

 wget https://developer.download.nvidia.com/compute/cuda/11.4.0/local_installers/cuda_11.4.0_470.42.01_linux.run
 sudo sh cuda_11.4.0_470.42.01_linux.run


 sudo /usr/local/cuda-10.1/bin/cuda-uninstaller
 sudo /usr/local/cuda-X.Y/bin/cuda-uninstaller

 sudo apt install nvidia-cuda-toolkit


 sudo apt-get --purge remove "*cublas*" "cuda*" "nsight*" 

Którą '''wersję''' SDK wybrać ? 
* The Streaming Multiprocessor (SM) version of your GPU determines which CUDA toolkit still supports it.
* if your compute capability is 6.1 us sm_61 and compute_61
* [https://developer.nvidia.com/cuda-gpus NVIDIA GPU Compute Capability]

===apt===

 sudo apt install cuda-toolkit
 sudo apt install nvidia-cuda-toolkit
 

=== wynik apt-get ===
* pakiet „cuda” nie jest częścią oficjalnego repozytorium Ubuntu

<pre>
sudo apt-get -y install cuda
Reading package lists... Done
Building dependency tree... Done
Reading state information... Done
Some packages could not be installed. This may mean that you have
requested an impossible situation or if you are using the unstable
distribution that some required packages have not yet been created
or been moved out of Incoming.
The following information may help to resolve the situation:

The following packages have unmet dependencies:
 libcufile-11-4 : Depends: liburcu6 but it is not installable
 libnvidia-decode-470 : Depends: libnvidia-compute-470 (= 470.42.01-0ubuntu1) but 470.199.02-0ubuntu0.22.04.1 is to be installed
 nvidia-driver-470 : Depends: libnvidia-compute-470 (= 470.42.01-0ubuntu1) but 470.199.02-0ubuntu0.22.04.1 is to be installed
                     Recommends: nvidia-prime (>= 0.8) but it is not going to be installed
                     Recommends: libnvidia-compute-470:i386 (= 470.42.01-0ubuntu1)
                     Recommends: libnvidia-decode-470:i386 (= 470.42.01-0ubuntu1)
                     Recommends: libnvidia-encode-470:i386 (= 470.42.01-0ubuntu1)
                     Recommends: libnvidia-ifr1-470:i386 (= 470.42.01-0ubuntu1)
                     Recommends: libnvidia-fbc1-470:i386 (= 470.42.01-0ubuntu1)
                     Recommends: libnvidia-gl-470:i386 (= 470.42.01-0ubuntu1)
 xserver-xorg-video-nvidia-470 : Depends: xorg-video-abi-24 but it is not installable or
                                          xorg-video-abi-23 but it is not installable or
                                          xorg-video-abi-20 but it is not installable or
                                          xorg-video-abi-19 but it is not installable or
                                          xorg-video-abi-18 but it is not installable or
                                          xorg-video-abi-15 but it is not installable or
                                          xorg-video-abi-14 but it is not installable or
                                          xorg-video-abi-13 but it is not installable or
                                          xorg-video-abi-12 but it is not installable or
                                          xorg-video-abi-11 but it is not installable or
                                          xorg-video-abi-10 but it is not installable or
                                          xorg-video-abi-8 but it is not installable or
                                          xorg-video-abi-6.0 but it is not installable
E: Unable to correct problems, you have held broken packages.

</pre>

===Wynik===
<pre>
Existing package manager installation of the driver found. It is strongly    │
│ recommended that you remove this before continuing.                          │
│ Abort                                                                        │
│ Continue  


Failed to verify gcc version. See log at /var/log/cuda-installer.log for details.

</pre>

W pliku /var/log/cuda-installer.log : 
* [INFO]: gcc version: gcc version 11.3.0 (Ubuntu 11.3.0-1ubuntu1~22.04.1) 
* [ERROR]: unsupported compiler version: 11.3.0.




W systemie jest:  gcc (Ubuntu 11.3.0-1ubuntu1~22.04.1) 11.3.0

Rozwiązanie problemu : 

<pre>
Enter CUDA Samples Location
 [ default is /home/a ]: 

Installing the CUDA Toolkit in /usr/local/cuda-8.0 ...
Missing recommended library: libXmu.so

Installing the CUDA Samples in /home/a ...
Copying samples to /home/a/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/a, but missing recommended libraries

Please make 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 ldconfig 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 did not install the CUDA Driver. A driver of version at least 361.00 is required for CUDA 8.0 functionality to work.
To install the driver using this installer, run the following command, replacing <CudaInstaller> with the name of this run file:
    sudo <CudaInstaller>.run -silent -driver

Logfile is /tmp/cuda_install_7699.log

</pre>

===Usuwanie starych wersji===

  sudo ./uninstall_cuda_8.0.pl

lub


 sudo apt-get --purge remove "*cublas*" "cuda*" "nsight*"



===pakiety===
Ubuntu:

 cat /var/lib/apt/lists/*cuda*Packages | grep "Package:" 

<pre>
Package: libcufft-11-4
Package: cuda-nsight-systems-11-4
Package: nvidia-driver-470
Package: nvidia-fabricmanager-dev-470
Package: cuda-cupti-11-4
Package: nvidia-fs
Package: libnvidia-gl-430
Package: nvidia-kernel-common-470
Package: libnvidia-decode-430
Package: cuda-cudart-11-4
Package: cuda-driver-dev-11-4
Package: xserver-xorg-video-nvidia-470
Package: cuda-toolkit-11-4
Package: libcusolver-dev-11-4
Package: cuda-memcheck-11-4
Package: nvidia-compute-utils-470
Package: libnvidia-decode-470
Package: cuda-nvml-dev-11-4
Package: libnvidia-fbc1-430
Package: cuda-compiler-11-4
Package: libnvidia-cfg1-470
Package: cuda-drivers-fabricmanager
Package: libcublas-dev-11-4
Package: cuda-documentation-11-4
Package: cuda-toolkit-config-common
Package: nvidia-settings
Package: cuda-nvtx-11-4
Package: nvidia-driver-430
Package: cuda-demo-suite-11-4
Package: libnvidia-fbc1-470
Package: libnpp-11-4
Package: nvidia-utils-430
Package: cuda-nvvp-11-4
Package: cuda-cuobjdump-11-4
Package: cuda-nvprof-11-4
Package: cuda-toolkit-11-config-common
Package: libcublas-11-4
Package: nvidia-compute-utils-430
Package: cuda-drivers-470
Package: cuda-gdb-src-11-4
Package: cuda-drivers
Package: cuda-cudart-dev-11-4
Package: cuda-cuxxfilt-11-4
Package: cuda-11-4
Package: libnvidia-extra-470
Package: libnvidia-common-430
Package: libnvidia-cfg1-430
Package: cuda-libraries-dev-11-4
Package: cuda-libraries-11-4
Package: libnvjpeg-dev-11-4
Package: libcufile-11-4
Package: libnvjpeg-11-4
Package: nvidia-utils-470
Package: nvidia-gds-11-4
Package: libcusparse-dev-11-4
Package: cuda-gdb-11-4
Package: nvidia-modprobe
Package: cuda-nsight-compute-11-4
Package: cuda-nvprune-11-4
Package: cuda
Package: xserver-xorg-video-nvidia-430
Package: libxnvctrl0
Package: cuda-nvcc-11-4
Package: libnvidia-encode-470
Package: nvidia-headless-no-dkms-470
Package: libnpp-dev-11-4
Package: cuda-drivers-fabricmanager-470
Package: nvidia-kernel-common-430
Package: nvidia-dkms-430
Package: libcurand-dev-11-4
Package: cuda-nvrtc-11-4
Package: libnvidia-compute-470
Package: libnvidia-ifr1-430
Package: libnvidia-ifr1-470
Package: nvidia-fs-dkms
Package: libnvidia-nscq-470
Package: nvidia-kernel-source-430
Package: libcurand-11-4
Package: cuda-compat-11-4
Package: cuda-visual-tools-11-4
Package: cuda-nvrtc-dev-11-4
Package: libnvidia-compute-430
Package: libnvidia-encode-430
Package: gds-tools-11-4
Package: cuda-cccl-11-4
Package: nvidia-dkms-470
Package: libxnvctrl-dev
Package: libcusolver-11-4
Package: cuda-samples-11-4
Package: cuda-sanitizer-11-4
Package: libnvidia-common-470
Package: nvidia-headless-470
Package: nvidia-headless-430
Package: nvidia-gds
Package: cuda-tools-11-4
Package: libcusparse-11-4
Package: nvidia-headless-no-dkms-430
Package: nsight-compute-2021.2.0
Package: cuda-minimal-build-11-4
Package: libnvidia-gl-470
Package: cuda-toolkit-11-4-config-common
Package: cuda-runtime-11-4
Package: nvidia-fabricmanager-470
Package: nvidia-kernel-source-470
Package: libcufft-dev-11-4
Package: cuda-nsight-11-4
Package: cuda-command-line-tools-11-4
Package: cuda-nvdisasm-11-4
Package: libcufile-dev-11-4
Package: cuda-cupti-dev-11-4
Package: nsight-systems-2021.2.4
</pre>

===Problemy===
* CUDA incompatible with my gcc version<ref>[https://stackoverflow.com/questions/6622454/cuda-incompatible-with-my-gcc-version stackoverflow question: cuda-incompatible-with-my-gcc-version]</ref>

==GPU==

Procesor GPU składa się z:
* multiprocesorów
* pamięci głównej karty graficznej

Multiprocesor składa się z:
* 8 lub 16 procesorów
*  pamięci multiprocesora (bardzo szybka, wspólna dla procesorów)

CUDA cores per SM: 
* 32 (Compute Capability 2.0) CUDA cores per SM
* 48 (2.1) CUDA cores per SM
* Compute Capability 3.0 (Kepler) now increases cores/SM hugely - to 192




==cuda wersje==
* 
*  Kepler-class GPUs and CUDA 5.0


Zobacz
* [https://docs.nvidia.com/deploy/cuda-compatibility/index.html docs.nvidia : cuda-compatibility]
* [https://stackoverflow.com/questions/53422407/different-cuda-versions-shown-by-nvcc-and-nvidia-smi stackoverflow question: different-cuda-versions-shown-by-nvcc-and-nvidia-smi]

Z linii poleceń (an.g command line  CLI ):<ref>[https://stackoverflow.com/questions/9727688/how-to-get-the-cuda-version stackoverflow question: how-to-get-the-cuda-version]</ref> 

 /usr/local/cuda/bin/nvcc --version  # gives the CUDA compiler version 
 nvcc --version #  the CUDA toolkit version 
 cat /usr/local/cuda/version.txt

Z poziomu aplikacja
* the runtime API version: cudaRuntimeGetVersion() 
* the driver API version: cudaDriverGetVersion()
* deviceQuery ( SDK sample app )

==api==
* driver api<ref>[https://docs.nvidia.com/cuda/cuda-runtime-api/driver-vs-runtime-api.html nvidia cuda: driver-vs-runtime-api]</ref>
* runtime api

==cuda related variables==

Zmienne<ref>[https://stackoverflow.com/questions/26976058/installing-cuda-for-linux-could-not-find-cuda stackoverflow question : installing-cuda-for-linux-could-not-find-cuda]</ref>
* CUDA_NVCC_EXECUTABLE 
* CUDA_INCLUDE_DIRS 
* CUDA_CUDART_LIBRARY
* CUDA_TOOLKIT_ROOT_DIR
* MAX_GCC_VERSION ( env var for that GCC version)

 export CUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda
 set(CUDA_TOOLKIT_ROOT_DIR "/usr/local/cuda")

==Typy liczb ==

* zmiennoprzecinkowe<ref>[http://forums.create.msdn.com/forums/p/43601/259010.aspx Increasing the float/double precision, how is this done? ]</ref>
** podwójnej precyzji są dostępne dla GPU o potencjale obliczeniowym 1.3 i większym  (ang. 1.3-capable card)
** double-double <ref>[http://forums.nvidia.com/index.php?showtopic=218452 Double double precision arithmetic library for Cuda now available]</ref>

Można wybrać potencjał obliczeniowy za pomocą opcji kompilatora: 

 nvcc --gpu-architecture sm_13 

lub
 
 -arch=sm_13

lub <ref>[https://www.cs.virginia.edu/~csadmin/wiki/index.php/CUDA_Support/Enabling_double-precision CUDA Support/Enabling double-precision from Computer Science Support Wiki ]</ref>

 --gpu-name sm_13

Lista dostępnych funkcji podwójnej precyzji w bibliotece CUDA<ref>[http://developer.download.nvidia.com/compute/cuda/4_2/rel/toolkit/docs/online/group__CUDA__MATH__DOUBLE.html Double Precision Mathematical Functions from NVIDIA CUDA Library Documentation 4.2]</ref>

==Struktura obliczeń==
Obliczenie K dzieli się na:
* gridy g składające się z bloków,
* bloki b, które dzielą się na wątki w.<ref>W Śmietanka : Programowanie na platformie CUDA. Delta 448 (9/2011)</ref> 

 <math>K = \{g_1, g_2, .. g_m \}</math> 

 <math>g_m = \{b_{m1}, b_{m2}, .. b_{mn} \}</math> 

 <math>b_i = \{w_{mi1}, w_{mi2}, .. w_{min} \}</math> 

==Struktura typowego programu CUDA==
* skopiować dane z pamięci komputera do pamięci karty graficznej
* skopiować dane do pamięci multiprocesora
* wykonać obliczenia na multiprocesorach
* skopiować częściowe wyniki z multiprocesorów do pamięci głównej karty graficznej
* skopiować końcowy wynik do pamięci komputera

[[File:CUDA processing flow (En).PNG|Struktura programu CUDA|ramka]]

==Jak zacząć==

Co potrzebujemy wg Nvidia<ref>[http://developer.nvidia.com/nvidia-gpu-computing-documentation NVIDIA GPU Computing Documentation]</ref>


Plik :<ref>[http://www.eti.pg.gda.pl/katedry/kask/dydaktyka/Przetwarzanie_Rownolegle_CUDA/CELL/01-cuda.pdf CUDA Programming
Paweł Czarnul]</ref>
* zapisujemy z rozszerzeniem ''cu''
 e.cu
* kompilujemy kompilatorem firmy NVIDIA: 
 nvcc e.cu 
* uruchamiamy: 
 ./a.out

==Programowanie ==
* [https://github.com/nvidia/cuda-samples nvidia cuda-samples]
* [https://docs.nvidia.com/cuda/demo-suite/index.html nvidia cuda demo-suite]
===Pierwszy program===
Przykładowy program pokazujący ilość dostępnych kart cuda<ref>[http://ubuntu.pl/forum/viewtopic.php?f=170&t=171080|Dyskusja na forum ubuntu - program użytkownika jablondyn]</ref>
<syntaxhighlight lang=c>
    #include <stdio.h>
    #include <cuda.h>

    int main(void)
    {

       int nDevices;
       
       cudaGetDeviceCount(&nDevices);
       printf("Number of CUDA devices = %d\n", nDevices);
       
       return 0;
    }

</syntaxhighlight>

Kompilacja:

 gcc c.c -Wall

daje efekt: 

 c.c:2:22: fatal error: cuda.h: Nie ma takiego pliku ani katalogu


Szukamy cuda.h:
  
  udpatedb
  locate cuda.h

otrzymujemy: 
<pre>
/usr/include/linux/cuda.h
/usr/src/linux-headers-3.11.0-12/include/linux/cuda.h
/usr/src/linux-headers-3.11.0-12/include/uapi/linux/cuda.h
/usr/src/linux-headers-3.11.0-12-generic/include/linux/cuda.h
/usr/src/linux-headers-3.11.0-13/include/linux/cuda.h
/usr/src/linux-headers-3.11.0-13/include/uapi/linux/cuda.h
/usr/src/linux-headers-3.11.0-13-generic/include/linux/cuda.h
/usr/src/linux-headers-3.11.0-14/include/linux/cuda.h
/usr/src/linux-headers-3.11.0-14/include/uapi/linux/cuda.h
/usr/src/linux-headers-3.11.0-14-generic/include/linux/cuda.h
/usr/src/linux-headers-3.11.0-15/include/linux/cuda.h
/usr/src/linux-headers-3.11.0-15/include/uapi/linux/cuda.h
/usr/src/linux-headers-3.11.0-15-generic/include/linux/cuda.h
</pre>

ale tu chodzi o katalog: 

 /usr/local/cuda/include/


który jest pusty ...
===hello.cu===
* [https://linuxconfig.org/how-to-check-cuda-version-on-ubuntu-20-04-focal-fossa-linux linuxconfig.org: hello.cu]
* [https://developer.nvidia.com/blog/easy-introduction-cuda-c-and-c/ A First CUDA C Program by NVIDIA]

===Linki===

* [http://www.digilanti.org/cudabrot/ cudabrot]
* Wykłady Wojciecha Kwedlo<ref>[http://aragorn.pb.bialystok.pl/~wkwedlo/PC-Slides.html Wykłady Wojciecha Kwedlo]</ref>
* wykłady wydziału ETI Politechniki Gdańskiej <ref>[http://www.eti.pg.gda.pl/katedry/kask/dydaktyka/Przetwarzanie_Rownolegle_CUDA/CELL/ wykłady wydziału ETI Politechniki Gdańskiej]</ref>
* zbiór Mandelbrota<ref>[http://www.slideshare.net/nowy20/generowanie-i-wizualizacja-zbioru-mandelbrota-na-cuda Generowanie i wizualizacja zbioru Mandelbrota na CUDA - Paweł Nowak]</ref>
* wady obliczeń za pomocą GPU <ref>[http://stackoverflow.com/questions/2569797/why-arent-we-programming-on-the-gpu Why aren't we programming on the GPU?]</ref>
* metoda Newtona dla zmiennej zespolonej <ref>[[http://www.smithbower.com/blog/2012/03/julia-set-for-the-complex-function-fz-z3-1-in-c-and-cuda/ Julia Set for the Complex Function f(z) = z^3 – 1, in C and CUDA by Stephen on March 4th, 2012]]</ref>
* [http://ecs.victoria.ac.nz/foswiki/pub/EResearch/EcsTeslaResource/Parallel.Computing.Using.Graphics.Cards.pdf  Parallel Computing Using GPUs Haizhen Wu]
* [http://event.cwi.nl/damon2010/gpuprecision.pdf GPU precision ]
* http://kmdouglass.github.io/stories/notes/cuda.html
* http://www.davidespataro.it/cuda-julia-set-fractals/
====biblioteki====
* precyzja
**[http://code.google.com/p/gpuprec/  gpurec - Supporting High Precision on Graphics Processors]
** GQD: double-double (31 decimal digits) i quad-double (62 decimal digits) precyzja na GPU<ref>[https://github.com/lumianph/gpuprec  github  gpuprec]</ref>
** GARPREC: dowolna precyzja
** [http://homepages.laas.fr/mmjoldes/campary/ campary/ Campary]
*** [https://devtalk.nvidia.com/default/topic/981023/library-for-complex-numbers-in-device-kernel/ links]
* liczby zespolone<ref>[https://stackoverflow.com/questions/17473826/cuda-how-to-work-with-complex-numbers?utm_medium=organic&utm_source=google_rich_qa&utm_campaign=google_rich_qa stackoverflow question : cuda-how-to-work-with-complex-numbers?]</ref>
** #include <cuComplex.h> , zwykle jest w /usr/local/cuda/include

==Słownik==
* cuda-gdb - The NVIDIA CUDA Debugger
*  nvcc - The NVIDIA CUDA Compiler
* cuobjdump - The NVIDIA CUDA Object Utility
* nvdisasm - The NVIDIA CUDA disassembler
* nvprune - The NVIDIA CUDA Prune Tool
* nsight - NVIDIA NSight, Eclipse Edition
* nvvp - The NVIDIA CUDA Visual Profiler
* nvprof - The NVIDIA CUDA Command-Line Profiler
* cuda-memcheck - The NVIDIA CUDA Check Tool
* API <ref>[http://home.agh.edu.pl/~socha/pmwiki/pmwiki.php/CUDA/CUDA Mirek SOcha : CUDA]</ref>
* blok = suma wątków wykonywanych przez pojedynczy multiprocesor
* device = (pl. urządzenie) = karta graficzna 
* grid = Wszystkie bloki pracujące nad jednym kernelem nazywamy gridem. Gridów może być maksymalnie tyle co multiprocesorów
* host = CPU  
* GPU = procesor karty graficznej
* CPU = procesor płyty głównej 
* kernel = 
** jądro kodu obliczeniowego 
** kod pojedynczego wątku CUDA<ref>Programowanie na platformie CUDA - Wojciech Śmietanka. Delta 448 (9/2011)</ref>
** "funkcja wykonywana na karcie graficznej równolegle przez CUDA-wątki" <ref>[http://www.knsi.pwr.wroc.pl/cuda/?p=163 Wstęp do CUDA Cz. II autor: ZimnowodaSzymon]</ref>
* nvcc - kompilator c firmy NVIDIA
* wątek = obliczenie wykonywane na pojedynczym procesorze GPU
* Cuda Cores are also called Stream Processors (SP)
* Streaming Multiprocessors (SM)
* grids which maps blocks to the GPU
* blocks which map threads to Stream Processors
* warp is always formed by 32 threads and all threads of a warp are executed simulaneously<ref>[https://stackoverflow.com/questions/3519598/streaming-multiprocessors-blocks-and-threads-cuda stackoverflow question: streaming-multiprocessors-blocks-and-threads-cuda]</ref>

==Pomoc==
* [https://askubuntu.com/questions/tagged/cuda askubuntu questions tagged cuda]
* [http://docs.nvidia.com/cuda/cuda-c-best-practices-guide/index.html nvidia: cuda-c-best-practices-guide]
* [https://stackoverflow.com/questions/tagged/cuda stackoverflow questions tagged cuda]
* [https://stackoverflow.com/tags/cuda/info stackoverflow tag info : cuda]

==Odnośniki==
<references/>