I am very beginner in CUDA. I just want to know how to read an image using CUDA programming? I am having NVidia Geforce 860 M, Installed Visual Studio 2012, CUDA 7.5.
Thanks in advance!!!
I think you can only do it via a CPU and then transfer the image to the CUDA board's memory. CUDA boards do not provide access to the file system.
You will need to read the file using C++ functions and then pass the pointer of the image pixels Array to CUDAMEMCPY function.This will transfer the image from the CPU memory to the GPU Global memory.
Related
I recently bought a dell XPS 15 with a i9 12900 HK, and downloaded Ubuntu 22.04 LTS as an OS.
I coded a simple OpenMP program, that should have a linear speedup in the number of threads (the code is correct because it behaves as expected when I run it on a cluster), but on my laptop, it stops the speedup at 6 threads, even if my processor has 20 threads. I made some research, and read that kernel 5.15 is not optimised for last Intel processors because it makes a bad use of P and E cores.
But I also read that it may be dangerous to update the kernel to a newer version like 5.17 or 5.18 because my GTX 3050 Ti drivers may not be compatible with this kernel.
Can I update the kernel safely ? Will it resolves my parallelism problem? What method should I use to update my kernel?
I tried to look to forums and docs, but lots of available documentation are from third parties and I don't know if I can trust them.
I have an OpenGL application that runs in Linux with an Intel GPU. I need to reduce the GPU memory consumption of my application. How can I profile effectively GPU memory?
I would like to be able to identify what parts are eating the most memory. For example, is it the textures or maybe the geometry? Is there any way I can query the memory that a texture or a buffer is consuming in OpenGL? Or are there any useful tools I can use for this?
I'm attempting my first electron App. Does anyone know why i get this error AVDCreateGPUAccelerator: Error loading GPU renderer in the terminal when i do npm start? It doesn't seem to effect anything yet. I've gotten it right from the beginning of my install using the quick start guide. Will it effect me down the road?
mac pro late 2013
2.7 GHz 12-Core Intel Xeon E5
128 GB 800 MHz DDR3 ECC
AMD FirePro D700 6144 MB
Most likely the AVDCreateGPUAccelerator error is because the Xeon CPUs used in the Mac Pros do not have a hardware decoder. Core i3/5/7 CPUs in Mac mini/iMac/Mac Book Pro) should work fine.
For a solution to this, please refer to the following answer;
https://stackoverflow.com/a/57405820/10868666
I have an nvidia GTX 750 Ti card, which is advertised as having 640 CUDA cores. Indeed, the nvidia settings application also reports this.
I'm trying to use this card to do OpenCL development on Linux. Now, I have reported from the OpenCL environment (through PyOpenCL if it makes a difference) that the number of compute units is 5. My understanding is that one compute unit on an nvidia device maps to one multiprocessor, which I understand to be 32 SIMD units (which I assume is the a CUDA core).
Clearly, 5 * 32 is not 640 (rather a quarter of what is expected).
Am I missing something as regards the meaning of a a work unit on nvidia? The card is also driving the graphics output which will be using some of the computational capability - is a proportion of the processing capability reserved for graphics use? (if so, can I change this?).
NVIDIA have a whitepaper for the NVIDIA GeForce GTX 750 Ti, which is worth a read.
An OpenCL compute unit translates to a streaming multiprocessor in NVIDIA GPU terms. Each Maxwell SMM in your GPU contains 128 processing elements ("CUDA cores") - and 128*5 = 640. The SIMD width of the device is still 32, but each compute unit (SMM) can issue instructions to four different warps at once.
I want to calculate the GPU load but have no idea which file contains that. Is it also stored somewhere like the CPU load in the /proc/loadavg file ?
For nVidia gpus see the thread: checking GPU load on nVidia hardware? [solved]
For intel chips just type:
su -c 'intel_gpu_top'
on the command line.