I am very very new to CUDA programming. I am going through the examples that came with the SDK. I was able to compile the code, but when I run it, I get the following error:
"clock.cu(177) : CUDA Runtime API error 38: no CUDA-capable device is detected".
I have GeForce 8400M GS on my machine, and according to this list, it is a CUDA-supported GPU: http://developer.nvidia.com/cuda-gpus.
There are several things you can check to fix that error:
Make sure that your /dev/nvidia*'s permission is 666
(crw-rw-rw-) and owner root:root
If you installed the SDK using sudo, then you may need to change
to root first to execute the code
Make sure you've installed the compatible NVIDIA's driver available in CUDA zone for your CUDA Toolkit
if you are sure cuda driver and all is installed then just try
$ sudo nvidia-xconfig --enable-all-gpus
now try ur cuda samples,
Related
I used below command to build binary for nvidia GPU:
clang++ -fsycl -fsycl-targets=nvptx64-nvidia-cuda simple-sycl-app.cpp -o simple-sycl-app-cuda
But got below error message:
clang++: error: cannot find 'libspirv-nvptx64--nvidiacl.bc'; provide path to libspirv library via '-fsycl-libspirv-path', or pass '-fno-sycl-libspirv' to build without linking with libspirv
I searched in both intel oneAPI installation path and cuda toolkit path, but cannot find the spirv-nvptx64-nvidiacl.bc.
Anyone knows where to find libspirv-nvptx64—nvidiacl.bc?
It looks like you are trying to compile using the DPC++ compiler for Nvidia GPUs.
This option is not included in the oneAPI release installations from the Intel website. At the moment you will need to compile the DPC++ LLVM project with this enabled to be able to use the appropriate flag to target Nvidia devices.
You can follow the instructions on this page to compile the project and then it explains how to use the ptx target. In the future Codeplay, the company I work for, intends to publish release binaries that include the ptx compiler option.
When I try to run nvprof command in Command Prompt, System Erros pops up and says "The code execution cannot proceed because cupti64_102.dll was not found. Reinstall the program may fix this problem."
I have installed the CUDA Toolkit 10.2 but cupti64_102.dll is not in /bin.
System: Windows 10, Quadro K4200, CUDA10.2, CUDA Toolkit 10.2
Just go to Environment Variables select path
Add the following string to your path
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2\extras\CUPTI\lib64
then nvprof will work as intended.
when asking for
nvidia-smi
it gives this error:
Failed to initialize NVML: GPU access blocked by the operating system
other information:
$ nvcc --verion
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2015 NVIDIA Corporation
Built on Mon_Feb_16_22:59:02_CST_2015
Cuda compilation tools, release 7.0, V7.0.27
and also:
$ lspci | grep -i nvidia
01:00.0 VGA compatible controller: NVIDIA Corporation GF108M [GeForce GT 425M] (rev a1)
01:00.1 Audio device: NVIDIA Corporation GF108 High Definition Audio Controller (rev a1)
Having searched a lot in the internet I couldn't find a way to solve this problem.
when I use ipython notebook and want to run Caffe framework it gives this error:
Check failed: error == cudaSuccess (38 vs. 0) no CUDA-capable device is detected
I noticed that after CUDA installation restarting Ubuntu works, and now I see the GPU details output by nvidia-smi
If you believe that both CUDA and graphics driver are installed correctly, but you still cannot make your GPU to be detected, the problem might be in that you are using mobile Nvidia graphics on Optimus-enabled laptop on Linux.
You could either:
change your application to properly detect GPUs behind Optimus. See documentation here
or to run your application via Bumblebee (and primus)
WSL user here. Running nvidia-smi on either Windows and WSL failed. Reinstalling the Nvidia for WSL driver, on the Windows side, fixed the problem. The problem was created when installing CUDA Toolkit and CUDNN broke the Nvidia for WSL driver.
I had the same problem. It was happened because of installing a nvidia toolkit (I am not sure). According to this website (which has useful ideas)
I found that cuda driver version in the cuda installer and host was incompatible. (host : 367.57 , installer: 375.26 , At first I could not check the installer version because all the versions was 367.57, but when I reinstall cuda by run file, I found it)
So, I uninstalled cuda and nvidia completely and install cuda again by this help. At first in the installation process I got some errors which I found, nvidia has not completely gone. After uninstalling completely, I installed cuda and now I can run "sudo nvidia-smi" without problem.
I got the error failed to initialize NVML: Driver/Library version mismatch. And nvidia-smi failed to print any info. I tried to find if there were other versions of nvidia driver installed in my ubuntu. But I just found nvidia-driver-390. In the end, reboot helped me solve the problem.
I'we been writing some simple cuda program (I'm student so I need to practice), and the thing is I can compile it with nvcc from terminal (using Kubuntu 12.04LTS) and then execute it with optirun ./a.out (hardver is geforce gt 525m on dell inspiron) and everything works fine. The major problem is that I can't do anything from Nsight. When I try to start debug version of code the message is "Launch failed! Binaries not found!". I think it's about running command with optirun but I'm not sure. Any similar experiences? Thanks, for helping in advance folks. :)
As this was the first post I found when searching for "nsight optirun" I just wanted to wanted to write down the steps I took to make it working for me.
Go to Run -> Debug Configurations -> Debugger
Find the textbox for CUDA GDB executable (in my case it was set to "${cuda_bin}/cuda-gdb")
Prepend "optirun --no-xorg", in my case it was then "optirun --no-xorg ${cuda_bin}/cuda-gdb"
The "--no-xorg" option might not be required or even counterproductive if you have an OpenGL window as it prevents any of that to appear. For my scientific code however it is required as it prevents me from running into kernel timeouts.
Happy bug hunting.
We tested Nsight on Optimus systems without optirun - see "Install the cuda toolkit" in CUDA Toolkit Getting Started on using CUDA toolkit on the Optimus system. We have not tried optirun with Nsight EE.
If you still need to use optirun for debugging, you can try making a shell script that uses optirun to start cuda-gdb and set that shell script as cuda-gdb executable in the debug configuration properties.
The simplest thing to do is to run eclipse with optirun, that will also run your app properly.
I'm trying to do some CUDA development on a PC without CUDA-capable GPU via emulation mode. The OS is Linux Mint Debian (can be considered Debian testing for all practical purposes) 32bit (2.6.32-5-686 kernel). Here's what I did so far:
Grabbed the CUDA Toolkit 32 bit and SDK for Ubuntu from http://developer.nvidia.com/cuda-toolkit-40
Installed the CUDA Toolkit in /usr/local/cuda/lib
Added the paths to bashrc
echo "# CUDA stuff
PATH=\$PATH:/usr/local/cuda/bin
LD_LIBRARY_PATH=\$LD_LIBRARY_PATH:/usr/local/cuda/lib
export PATH
export LD_LIBRARY_PATH" >> ~/.bashrc
Added the path to /etc/ld.so.conf.d/cuda.conf:
/usr/local/cuda/lib
Executed "sudo ldconfig"
Restarted the session
Then installed the SDK in /home/user/NVIDIA_GPU_Computing_SDK folder
When I got to NVIDIA_GPU_Computing_SDK/C and type "make emu=1" to compile the examples I get:
nvcc warning : option 'device-emulation' has been deprecated and is ignored
/usr/bin/ld: cannot find -lcudartemu
/usr/bin/ld: cannot find -lcudartemu
collect2: ld returned 1 exit status
Seems like a library missing (rt = runtime ?). There is libcudart3 in the package manager, but wants a whole bunch of nvidia stuff as a dependency, including drivers and I don't even have an NVIDIA card on this machine. Also apparently the GPU emulation is now deprecated... Does anybody have some experience with CUDA emulation?
There is no emulation in CUDA any more. It was deprecated and removed during the 3.x release cycle. There is no emulation support beyond CUDA 3.1 IIRC. Certainly there is nothing you can do in CUDA 4.0.
On Linux, your best bet is to try gpuocelot, which provides a PTX level emulation on x86 processors and a reimplementation of the CUDA APIs.
Although I agree with the suggestion to try Ocelot, when I was in the same boat I found it easiest to go on eBay and get a cheap CUDA capable card to use for testing (I think I paid < $40). If you have the ability to open the hardware (I realize this isn't an option for some people) and to install drivers, that's what I'd suggest.