Matlab Does not recognise NVIDIA GPU Card in the PC

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Mammo Image
Mammo Image 2017-10-6
评论： Edric Ellis 2023-12-7
I am trying to use GPU NVIDIA Quadro 2000 with matlab 2017a but every time I am using gpuDeviceCount the answer is 0.
I have tried to update the driver of the gpu but the version was the latest one.
wish to solve my problem Thanks
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Mammo Image
Mammo Image 2017-10-6
Is there any wrong in the question? I feel there is something misunderstood!!
Walter Roberson
Walter Roberson 2017-10-6
Questions here are answered by volunteers. There are currently over 140 new questions being asked every day, and activity on about another 100 older questions, so there is activity on over 225 questions every day. Sometimes volunteers overlook questions; sometimes they just do not have time to look at over 225 questions every day -- a lot of the volunteers also have paid jobs that they have to attend to.
And... not all of the volunteers know everything about every topic, so whether you get a response or not can depend on whether the right volunteer happens to read your Question.
I have personally worked on over 65 different Questions within the last 24 hours. Your Question came in while I was sleeping, as did your reminder prompt about 2 hours later; when I woke up, I had a responsibility to deal first with the Questions I was already working on. With all the new Questions coming in, I did not see your Question until now.

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回答（2 个）

Walter Roberson
Walter Roberson 2017-10-6
Is that:
  • Quadro P2000 (CUDA capability 6.1)
  • Quadro M2000 (CUDA capability 5.3)
  • Quadro K2000 (CUDA capability 3.0)
What operating system are you using, and which distribution / release are you using? Which CUDA did you install?
If you just recently installed CUDA then you would have downloaded CUDA 9.0, which was not supported by MATLAB R2017a. MATLAB R2017a supported CUDA 8.0; you can download that from https://developer.nvidia.com/cuda-80-ga2-download-archive
Edric Ellis
Edric Ellis 2017-10-9
编辑：Edric Ellis 2017-10-9
Further to Walter's pertinent questions, I'd like to add: it's important to distinguish the CUDA driver from the CUDA toolkit.
CUDA Driver:
  • The CUDA driver allows the computer to access the GPU device for computation
  • All users must install a CUDA driver
  • It is always advisable to use the latest CUDA driver (NVIDIA ensure backwards-compatibility for CUDA drivers which mean that newer drivers are intended to work completely correctly with older applications)
CUDA Toolkit:
  • The CUDA toolkit provides support for compiling CUDA sources
  • Most users don't actually need to install the CUDA toolkit
  • The CUDA toolkit is needed only for compiling CUDA sources (e.g. for use with mexcuda or CUDAKernel)
  • If you do install the CUDA toolkit, ensure that the version matches the version used by MATLAB. The version used by MATLAB is described in the Parallel Computing Toolbox release notes
If after installing the latest CUDA driver, MATLAB cannot recognise the GPU device, then contact MathWorks support who are able to take you through additional debugging steps.
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pb
pb 2023-12-7
Hi @Edric Ellis, thanks very much for your quick response, I was traveling so missed to acknowledge it earlier. I did update the driver and it was detected in MATLAB. I had not messed with the driver because the person who installed the Graphics Card had installed that driver.
As per your suggetsion I will also look for another graphics card, is there any main specs I should look out for?
Finally, if you get a moment, would you be able to take a look at my comment on here, from 25 Nov 2023, and if you could shed any light on it that would be great. The documentation for GPU usage states that all inputs must be moved onto GPU in order to compute on the GPU, however, I am trying to figure out what the nature of the inputs are (i.e. a computing tool such as a classifier, versus a simple variable that may be altered)
Edric Ellis
Edric Ellis 2023-12-7
GPU specs depend on the workload you're intending to use. Standard MATLAB-style numerics require good double-precision performance. These devices can be very expensive. Other applications use integer or single-precision floating point numbers far more, and can run well on somewhat less expensive devices.
Regarding your comment - in general you need to move data to the GPU to have operations performed there - but this only applies to numeric/logical data and ordinary numeric operations. Deep learning isn't something I know much about, but perhaps this page will help: https://uk.mathworks.com/help/deeplearning/ug/scale-up-deep-learning-in-parallel-on-gpus-and-in-the-cloud.html

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