Is there is anyway to get the GPU processor usage using CUDA. I want to get the processor usage of each GPU connected in a cluster and to assign the job to the GPU having least processor usage.
Operating system i am using is Windows 7 64bit. All the connected GPUs have fermi architecture
Please help.
NVIDIA Management Library is a C-based API for monitoring and managing various states of the NVIDIA GPU devices. It provides a direct access to the queries and commands exposed via the cmdline tool nvidia-smi.
https://developer.nvidia.com/nvidia-management-library-nvml
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I have had created VM instances using google cloud platform (using console). The VM is a based on WINDOWS SERVER 2019! I have been successful in making one but unable to get some virtual ram in the instances. It (VRAM) shows zero. Does adding GPU not increase the vram? If not then what increases them? I am looking to increase the same for gaming purposes and using software like ADOBE AND AUTODESK too...
Instances created with additional GPU's (Like Tesla K80 and other) have all specified amount of GPU memory (VRAM).
You can find list of all GPU's in the documentation.
Every GPU has an amount of memory specified in the table.
If you create a VM with one K80 GPU it will kave 16GB of DDR6 memory available (nothing to do with the type of the machine or actual RAM assigned).
You can find how much of VRAM a GPU has in the Device manager; find "Display adapters" and expand it and find your card; it's all in the "general" tab.
And regarding any Adobe or Autodesk software I can't really tell if having Tesla will be of advantage..
Is there a way to trace system calls related to DMA transfers with strace command in Linux. I use the Zedboard which consist of FPGA and ARM A9 Core. I use the Linardo version of Linux (Linaro 12.11) where tool support is not available for using perf command (No package error- when installing). Please share any alternative commands that can help in tracing system calls related to DMA transfers. In my case, I have an application running on Linux where it transfer data with FPGA via DMA (which is the part of interest).
I have recently purchased a development board utilizing Samsung Exynos5422 application processor (Cortex™-A15 2.0Ghz quad core and Cortex™-A7 quad core CPUs). I have tried to extract the performance counters in android using perf v3.0.8; however, none of the counters outputs a value (They are all "not counted"). Does anyone know how to solve this issue?
(The kernel version is 3.10.9)
Iam using nvidia gt 440 gpu. It is used for both display and computational purpose which leads to less performance while computation. can i enable it only for computational purpose? if so how can i disable it from using display.
It depends -- are you working on Windows or Linux? Do you have any other display adapters (graphics cards) in the machine?
If you're on Linux, you can run without the X Windows Server (i.e., from a terminal) and SSH into the box (or attach your display to another adapter).
If you're on Windows, you need to have a second display adapter. As long as your display is connected to your GeForce 440 GT, there's no way to use it only for computational purposes. That also includes Remote Desktop, which won't work at all unless you have a Tesla card because of the way the WDDM (Windows Display Driver Model) was designed (it can't be accessed from within Session 0, which is where the RDP service runs).
I'm using Intel integrated graphics for display purposes and GPU for compute purpose on Linux.
You'll need to setup from bios to use the integrated graphics on mobo. This will leave your GPU free. It depends on your hardware available. =)
How much does it affects the performance? I did checked before, the display in windows did takes up some memory (less than 10mb).
Check that you have write permission on the /dev/nvidia* devices. The CUDA C Getting Started Guide for Linux contains a script that automatically sets the correct permissions at startup.
I have seen this question many times but never found an answer for Windows.
I recently ported my CUDA code to OpenCL.
When testing with an ATI card, the Catalyst drivers contain a CPU OpenCL driver, hence I can run the OpenCL code on the CPU.
When testing with an NVIDIA card, there is no driver for the CPU.
Question is: how can I install (and deploy) a CPU driver when running with an Nvidia card?
Thanks a lot
To use OpenCL on CPU you don't need any driver, you only need OpenCL runtime that supports CPU, which (in case of AMD/ATI) is part of APP SDK. It could be installed no matter what GPU you have. Your end-users would also have to install the APP SDK: currently, there is no way to install OpenCL runtime only.
If you have Intel CPU, you better try Intel OpenCL SDK, which has separate installer. However, AMD APP SDK works on Intel CPUs quite well, but note vice versa.