nvidia

问题 What is the internal operation that allows nvidia-smi fetch the hardware level details? The tool executes even when some process is already running on the GPU device and gets the utilization details, name and id of the process etc. Is it possible to develop such a tool at the user level? How is NVML related? 回答1: Nvidia-smi is a thin wrapper around NVML. You can code with NVML with help of SDK contained in Tesla Deployment Kit. Everything that can be done with nvidia-smi can be queried

问题 What is the internal operation that allows nvidia-smi fetch the hardware level details? The tool executes even when some process is already running on the GPU device and gets the utilization details, name and id of the process etc. Is it possible to develop such a tool at the user level? How is NVML related? 回答1: Nvidia-smi is a thin wrapper around NVML. You can code with NVML with help of SDK contained in Tesla Deployment Kit. Everything that can be done with nvidia-smi can be queried

问题 We are looking for some advice with slurm salloc gpu allocations. Currently, given: % salloc -n 4 -c 2 -gres=gpu:1 % srun env | grep CUDA CUDA_VISIBLE_DEVICES=0 CUDA_VISIBLE_DEVICES=0 CUDA_VISIBLE_DEVICES=0 CUDA_VISIBLE_DEVICES=0 However, we desire more than just device 0 to be used. Is there a way to specify an salloc with srun/mpirun to get the following? CUDA_VISIBLE_DEVICES=0 CUDA_VISIBLE_DEVICES=1 CUDA_VISIBLE_DEVICES=2 CUDA_VISIBLE_DEVICES=3 This is desired such that each task gets 1

问题 We are looking for some advice with slurm salloc gpu allocations. Currently, given: % salloc -n 4 -c 2 -gres=gpu:1 % srun env | grep CUDA CUDA_VISIBLE_DEVICES=0 CUDA_VISIBLE_DEVICES=0 CUDA_VISIBLE_DEVICES=0 CUDA_VISIBLE_DEVICES=0 However, we desire more than just device 0 to be used. Is there a way to specify an salloc with srun/mpirun to get the following? CUDA_VISIBLE_DEVICES=0 CUDA_VISIBLE_DEVICES=1 CUDA_VISIBLE_DEVICES=2 CUDA_VISIBLE_DEVICES=3 This is desired such that each task gets 1

问题 My development workstation(s) currently have NVIDIA Quadro K2200 and K620. Both of which have CUDA compute capability 5.0. However, the final production system has a Tesla K80 which has CUDA compute capability 3.7. Is it possible to install and develop CUDA programs for compute capability 3.7 on my Quadro GPUs and then move them to the K80 without having to make significant changes? 回答1: Yes, it's possible. Be sure not to use any compute capability 5.0+ specific features in your code, and you

问题 My development workstation(s) currently have NVIDIA Quadro K2200 and K620. Both of which have CUDA compute capability 5.0. However, the final production system has a Tesla K80 which has CUDA compute capability 3.7. Is it possible to install and develop CUDA programs for compute capability 3.7 on my Quadro GPUs and then move them to the K80 without having to make significant changes? 回答1: Yes, it's possible. Be sure not to use any compute capability 5.0+ specific features in your code, and you

问题 My development workstation(s) currently have NVIDIA Quadro K2200 and K620. Both of which have CUDA compute capability 5.0. However, the final production system has a Tesla K80 which has CUDA compute capability 3.7. Is it possible to install and develop CUDA programs for compute capability 3.7 on my Quadro GPUs and then move them to the K80 without having to make significant changes? 回答1: Yes, it's possible. Be sure not to use any compute capability 5.0+ specific features in your code, and you

问题 What are the steps to get tensorflow-gpu 2.x Python package working on Windows with an NVidia GPU? I.e. how can I get rid of Could not find 'cudart64_101.dll' and then Could not find 'cudnn64_7.dll' ? 回答1: Steps Requires specific versions according to the error messages you see, not latest versions! 1. Download and install latest NVidia driver https://www.nvidia.com/Download/index.aspx 2. Install Tensorflow Python package pip uninstall tensorflow pip install tensorflow-gpu 3. Test At first

问题 I try to change my gamma of just one screen and not all my screens. I use this code to help me But this SetDeviceGammaRamp(GetDC(IntPtr.Zero), ref s_ramp); Is for all devices. [EDIT2] I saw one weird thing : SetDeviceGammaRamp is not the same gamma of the Nvidia Panel Controller (I tried to change my value of SetDeviceGammaRamp, and it's like if i changed the value of brightness and contrast in the Nvidia panel). So i think i must to use NVidia API :/ So, how can i change this code to put my

问题 I am running a memory coalescing experiment on Pascal and getting unexpected nvprof results. I have one kernel that copies 4 GB of floats from one array to another one. nvprof reports confusing numbers for gld_transactions_per_request and gst_transactions_per_request . I ran the experiment on a TITAN Xp and a GeForce GTX 1080 TI. Same results. #include <stdio.h> #include <cstdint> #include <assert.h> #define N 1ULL*1024*1024*1024 #define gpuErrchk(ans) { gpuAssert((ans), __FILE__, __LINE__);