Author Topic: NVIDIA 310M su sony Vaio VPCS11E7E  (Read 6694 times)

bi7ext

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on: February 04, 2011, 05:56:03 PM
Ciao a tutti, da tempo ho un problema che mi fa storcere il naso, ho comprato un Sony Vaio VPCS11E7E, davvero un bel portatilino :D peccato che non ho ancora trovato un modo per installare i driver proprietari della NVIDIA (GeForce 310M) su linux. Leggendo qua e là in rete ho trovato molti utenti con il mio stesso problema, alcuni di loro hanno affermato di aver risolto il problema, però anche seguendo le guide indicate il massimo che ho ottenuto è stato uno schermo totalmente azzurro :S
avete quache suggerimento??? sto impazzendo :-\

Grazie

bi7ext



clshack

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Reply #1 on: February 04, 2011, 06:08:46 PM
potresti postare i tuoi errori ;) ? [oltre alla schermata blu]



bi7ext

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Reply #2 on: February 04, 2011, 06:25:21 PM
potresti postare i tuoi errori ;) ? [oltre alla schermata blu]

non ho proprio errori, lo schermo rimane nero (morto) appena dopo aver installato i driver :S qualsiasi distribuzione linux mi parte solo in modalità nomodeset... non so proprio dove sbattere la testa  :-[



ghostdog

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Reply #3 on: February 04, 2011, 07:02:52 PM
 :D Ciao
Installare Cuda drivers e' un grattacapo !! conosco benissimo le frustrazioni, e' un procedimento che si deve prendere alla lettera senza trascurare nessun dettaglio.
Segui questa guida, e andra' tutto bene!

Go to the official nvidia-CUDA download page:
http://developer.nvidia.com/object/cuda_3_0_downloads.html#Linux

Download the CUDA Toolkit and the CUDA SDK:

CUDA Toolkit for Ubuntu Linux 9.04 and higher (32-bit)
GPU Computing SDK code samples and more

-----------------------------
Installing the NVIDIA driver:
-----------------------------
We'll need the latest cuda development driver available (195.xx), but first we'll uninstall the existing drivers.

1. Uninstall existing NVIDIA drivers and nvidia-glx.

(if you have enabled nvidia in system->administration->hardware drivers, then disable them first and possibly reboot)
sudo apt-get purge nvidia-*

2. Install driver:

press CTRL+ALT+F1, login, run

$ sudo service gdm stop

3. Install drivers from nvidia repository

sudo add-apt-repository ppa:nvidia-vdpau/ppa
sudo apt-get update
sudo apt-get install nvidia-185-modaliases nvidia-glx-185 nvidia-settings
sudo nvidia-xconfig

4. Reboot and log back in.

5. Run

$ nvidia-settings

to verify that your driver version is at least 195. Look for the driver version in the window:
The 195.xx NVIDIA Driver for use with CUDA.

----------------------------
Installing the CUDA Toolkit:
----------------------------

After having installed the driver we now need to install the CUDA toolkit itself.

1. Run:

$ sudo sh cudatoolkit_3.0_linux_32_ubuntu9.04.run

2. Press enter to install at the default location.

/usr/local/cuda

3. Register the new library files:

$ sudo gedit /etc/ld.so.conf.d/cuda.conf &

and add the following to the empty file

/usr/local/cuda/lib

Save the file and close gedit.
Then run:

$ sudo ldconfig

Create a link to the libcuda.so library:

cd /usr/lib
sudo ln -s nvidia-current/libcuda.so libcuda.so

Also add to the end of your ~/.bashrc file.

export PATH=/usr/local/cuda/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda/lib

restart bash

----------------------------------------------------------
Installing the CUDA SDK and Compiling the Example Programs
----------------------------------------------------------

We will now install the CUDA SDK to our own home directory (we can experiment with the supplied demos):

1. Install SDK to the default location

$ sh gpucomputingsdk_3.0_linux.run

2. As CUDA does not yet work with GCC 4.4 we will have to install gcc-4.3:

sudo apt-get install gcc-4.3 g++-4.3 g++-4.4
sudo update-alternatives --remove-all gcc
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-4.3 43 --slave /usr/bin/g++ g++ /usr/bin/g++-4.3 --slave /usr/bin/gcov gcov /usr/bin/gcov-4.3
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-4.4 44 --slave /usr/bin/g++ g++ /usr/bin/g++-4.4 --slave /usr/bin/gcov gcov /usr/bin/gcov-4.4
sudo update-alternatives --config gcc ### choose gcc 4.3

3. Install CUDA SDK requirements

sudo apt-get install libglut3-dev libxi-dev libxmu-dev

4. Go to SDK source dir:

cd ~/NVIDIA_GPU_Computing_SDK/C$

5. You should now be able to compile everything by running

$ make

This should now compile all the examples in the SDK without errors.

---------------------------------------------
Verify Installation
---------------------------------------------

We can now verify that everything is working:

1. Run (from ~/NVIDIA_GPU_Computing_SDK/C):

$ bin/linux/release/deviceQuery

On my machine I get the following output (depending on your harware, you output may be different. mine is a GeForce 8500 GT):

------------------------------------------------
bin/linux/release/deviceQuery Starting...

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

There is 1 device supporting CUDA

Device 0: "GeForce 8500 GT"
CUDA Driver Version: 3.0
CUDA Runtime Version: 3.0
CUDA Capability Major revision number: 1
CUDA Capability Minor revision number: 1
Total amount of global memory: 536150016 bytes
Number of multiprocessors: 2
Number of cores: 16
Total amount of constant memory: 65536 bytes
Total amount of shared memory per block: 16384 bytes
Total number of registers available per block: 8192
Warp size: 32
Maximum number of threads per block: 512
Maximum sizes of each dimension of a block: 512 x 512 x 64
Maximum sizes of each dimension of a grid: 65535 x 65535 x 1
Maximum memory pitch: 2147483647 bytes
Texture alignment: 256 bytes
Clock rate: 1.57 GHz
Concurrent copy and execution: Yes
Run time limit on kernels: Yes
Integrated: No
Support host page-locked memory mapping: No
Compute mode: Default (multiple host threads can use this device simultaneously)

deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 134566327, CUDA Runtime Version = 3.0, NumDevs = 1, Device = GeForce 8500 GT


PASSED

Press to Quit...

Spero che questo ti aiuta  ;)



bi7ext

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Reply #4 on: February 04, 2011, 09:25:16 PM
:D Ciao
Installare Cuda drivers e' un grattacapo !! conosco benissimo le frustrazioni, e' un procedimento che si deve prendere alla lettera senza trascurare nessun dettaglio.
Segui questa guida, e andra' tutto bene!

Go to the official nvidia-CUDA download page:
http://developer.nvidia.com/object/cuda_3_0_downloads.html#Linux

Download the CUDA Toolkit and the CUDA SDK:

CUDA Toolkit for Ubuntu Linux 9.04 and higher (32-bit)
GPU Computing SDK code samples and more

-----------------------------
Installing the NVIDIA driver:
-----------------------------
We'll need the latest cuda development driver available (195.xx), but first we'll uninstall the existing drivers.

1. Uninstall existing NVIDIA drivers and nvidia-glx.

(if you have enabled nvidia in system->administration->hardware drivers, then disable them first and possibly reboot)
sudo apt-get purge nvidia-*

2. Install driver:

press CTRL+ALT+F1, login, run

$ sudo service gdm stop

3. Install drivers from nvidia repository

sudo add-apt-repository ppa:nvidia-vdpau/ppa
sudo apt-get update
sudo apt-get install nvidia-185-modaliases nvidia-glx-185 nvidia-settings
sudo nvidia-xconfig

4. Reboot and log back in.

5. Run

$ nvidia-settings

to verify that your driver version is at least 195. Look for the driver version in the window:
The 195.xx NVIDIA Driver for use with CUDA.

----------------------------
Installing the CUDA Toolkit:
----------------------------

After having installed the driver we now need to install the CUDA toolkit itself.

1. Run:

$ sudo sh cudatoolkit_3.0_linux_32_ubuntu9.04.run

2. Press enter to install at the default location.

/usr/local/cuda

3. Register the new library files:

$ sudo gedit /etc/ld.so.conf.d/cuda.conf &

and add the following to the empty file

/usr/local/cuda/lib

Save the file and close gedit.
Then run:

$ sudo ldconfig

Create a link to the libcuda.so library:

cd /usr/lib
sudo ln -s nvidia-current/libcuda.so libcuda.so

Also add to the end of your ~/.bashrc file.

export PATH=/usr/local/cuda/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda/lib

restart bash

----------------------------------------------------------
Installing the CUDA SDK and Compiling the Example Programs
----------------------------------------------------------

We will now install the CUDA SDK to our own home directory (we can experiment with the supplied demos):

1. Install SDK to the default location

$ sh gpucomputingsdk_3.0_linux.run

2. As CUDA does not yet work with GCC 4.4 we will have to install gcc-4.3:

sudo apt-get install gcc-4.3 g++-4.3 g++-4.4
sudo update-alternatives --remove-all gcc
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-4.3 43 --slave /usr/bin/g++ g++ /usr/bin/g++-4.3 --slave /usr/bin/gcov gcov /usr/bin/gcov-4.3
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-4.4 44 --slave /usr/bin/g++ g++ /usr/bin/g++-4.4 --slave /usr/bin/gcov gcov /usr/bin/gcov-4.4
sudo update-alternatives --config gcc ### choose gcc 4.3

3. Install CUDA SDK requirements

sudo apt-get install libglut3-dev libxi-dev libxmu-dev

4. Go to SDK source dir:

cd ~/NVIDIA_GPU_Computing_SDK/C$

5. You should now be able to compile everything by running

$ make

This should now compile all the examples in the SDK without errors.

---------------------------------------------
Verify Installation
---------------------------------------------

We can now verify that everything is working:

1. Run (from ~/NVIDIA_GPU_Computing_SDK/C):

$ bin/linux/release/deviceQuery

On my machine I get the following output (depending on your harware, you output may be different. mine is a GeForce 8500 GT):

------------------------------------------------
bin/linux/release/deviceQuery Starting...

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

There is 1 device supporting CUDA

Device 0: "GeForce 8500 GT"
CUDA Driver Version: 3.0
CUDA Runtime Version: 3.0
CUDA Capability Major revision number: 1
CUDA Capability Minor revision number: 1
Total amount of global memory: 536150016 bytes
Number of multiprocessors: 2
Number of cores: 16
Total amount of constant memory: 65536 bytes
Total amount of shared memory per block: 16384 bytes
Total number of registers available per block: 8192
Warp size: 32
Maximum number of threads per block: 512
Maximum sizes of each dimension of a block: 512 x 512 x 64
Maximum sizes of each dimension of a grid: 65535 x 65535 x 1
Maximum memory pitch: 2147483647 bytes
Texture alignment: 256 bytes
Clock rate: 1.57 GHz
Concurrent copy and execution: Yes
Run time limit on kernels: Yes
Integrated: No
Support host page-locked memory mapping: No
Compute mode: Default (multiple host threads can use this device simultaneously)

deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 134566327, CUDA Runtime Version = 3.0, NumDevs = 1, Device = GeForce 8500 GT


PASSED

Press to Quit...

Spero che questo ti aiuta  ;)

Grazie mille per la guida stradettagliata 8) ora son di fretta domani pomeriggio provo a fare tutto ;)

ps: non ho visto riferimenti a configurazioni manuali del file xorg.conf, quindi non va editato? azz pensavo dipendesse tutto da quel maledetto file l'ho modificato in quasi tutti i modi :D

bi7ext



bi7ext

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Reply #5 on: February 07, 2011, 11:27:23 AM
:D Ciao
Installare Cuda drivers e' un grattacapo !! conosco benissimo le frustrazioni, e' un procedimento che si deve prendere alla lettera senza trascurare nessun dettaglio.
Segui questa guida, e andra' tutto bene!

Go to the official nvidia-CUDA download page:
http://developer.nvidia.com/object/cuda_3_0_downloads.html#Linux

Download the CUDA Toolkit and the CUDA SDK:

CUDA Toolkit for Ubuntu Linux 9.04 and higher (32-bit)
GPU Computing SDK code samples and more

-----------------------------
Installing the NVIDIA driver:
-----------------------------
We'll need the latest cuda development driver available (195.xx), but first we'll uninstall the existing drivers.

1. Uninstall existing NVIDIA drivers and nvidia-glx.

(if you have enabled nvidia in system->administration->hardware drivers, then disable them first and possibly reboot)
sudo apt-get purge nvidia-*

2. Install driver:

press CTRL+ALT+F1, login, run

$ sudo service gdm stop

3. Install drivers from nvidia repository

sudo add-apt-repository ppa:nvidia-vdpau/ppa
sudo apt-get update
sudo apt-get install nvidia-185-modaliases nvidia-glx-185 nvidia-settings
sudo nvidia-xconfig

4. Reboot and log back in.

5. Run

$ nvidia-settings

to verify that your driver version is at least 195. Look for the driver version in the window:
The 195.xx NVIDIA Driver for use with CUDA.

----------------------------
Installing the CUDA Toolkit:
----------------------------

After having installed the driver we now need to install the CUDA toolkit itself.

1. Run:

$ sudo sh cudatoolkit_3.0_linux_32_ubuntu9.04.run

2. Press enter to install at the default location.

/usr/local/cuda

3. Register the new library files:

$ sudo gedit /etc/ld.so.conf.d/cuda.conf &

and add the following to the empty file

/usr/local/cuda/lib

Save the file and close gedit.
Then run:

$ sudo ldconfig

Create a link to the libcuda.so library:

cd /usr/lib
sudo ln -s nvidia-current/libcuda.so libcuda.so

Also add to the end of your ~/.bashrc file.

export PATH=/usr/local/cuda/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda/lib

restart bash

----------------------------------------------------------
Installing the CUDA SDK and Compiling the Example Programs
----------------------------------------------------------

We will now install the CUDA SDK to our own home directory (we can experiment with the supplied demos):

1. Install SDK to the default location

$ sh gpucomputingsdk_3.0_linux.run

2. As CUDA does not yet work with GCC 4.4 we will have to install gcc-4.3:

sudo apt-get install gcc-4.3 g++-4.3 g++-4.4
sudo update-alternatives --remove-all gcc
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-4.3 43 --slave /usr/bin/g++ g++ /usr/bin/g++-4.3 --slave /usr/bin/gcov gcov /usr/bin/gcov-4.3
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-4.4 44 --slave /usr/bin/g++ g++ /usr/bin/g++-4.4 --slave /usr/bin/gcov gcov /usr/bin/gcov-4.4
sudo update-alternatives --config gcc ### choose gcc 4.3

3. Install CUDA SDK requirements

sudo apt-get install libglut3-dev libxi-dev libxmu-dev

4. Go to SDK source dir:

cd ~/NVIDIA_GPU_Computing_SDK/C$

5. You should now be able to compile everything by running

$ make

This should now compile all the examples in the SDK without errors.

---------------------------------------------
Verify Installation
---------------------------------------------

We can now verify that everything is working:

1. Run (from ~/NVIDIA_GPU_Computing_SDK/C):

$ bin/linux/release/deviceQuery

On my machine I get the following output (depending on your harware, you output may be different. mine is a GeForce 8500 GT):

------------------------------------------------
bin/linux/release/deviceQuery Starting...

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

There is 1 device supporting CUDA

Device 0: "GeForce 8500 GT"
CUDA Driver Version: 3.0
CUDA Runtime Version: 3.0
CUDA Capability Major revision number: 1
CUDA Capability Minor revision number: 1
Total amount of global memory: 536150016 bytes
Number of multiprocessors: 2
Number of cores: 16
Total amount of constant memory: 65536 bytes
Total amount of shared memory per block: 16384 bytes
Total number of registers available per block: 8192
Warp size: 32
Maximum number of threads per block: 512
Maximum sizes of each dimension of a block: 512 x 512 x 64
Maximum sizes of each dimension of a grid: 65535 x 65535 x 1
Maximum memory pitch: 2147483647 bytes
Texture alignment: 256 bytes
Clock rate: 1.57 GHz
Concurrent copy and execution: Yes
Run time limit on kernels: Yes
Integrated: No
Support host page-locked memory mapping: No
Compute mode: Default (multiple host threads can use this device simultaneously)

deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 134566327, CUDA Runtime Version = 3.0, NumDevs = 1, Device = GeForce 8500 GT


PASSED

Press to Quit...

Spero che questo ti aiuta  ;)

Rieccomi, purtroppo non riesco a superare la maledetta installazione dei driver nvidia, stesso problema citato sopra  :-\

Grazie comunque ;)

bi7ext