I am new to gminer, can anyone tell me why it is verifying on CPU instead of GPU?
verifying Shares on processor, warning when GPU overclocking is very high for Ethash, Etchash, KAWPOW and ProgPoW algorithms, helps to overclock GPU without errors
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I'm trying to start multiple processes (10 for example) of learning with tensorflow 2. I'm still using Session and so on multiple tf.compat.v1 in all my codebase.
When I'm running with CPU, processes take each around 500mo of CPU memory. htop output :
When I'm running with GPU, processes take each much more CPU memory (like 3Go each) and almost the same (more in reality) GPU memory. nvtop output (GPU mem left, CPU (HOST) mem right) :
I can reduce GPU memory process fingerprint by using environment variable TF_CUDNN_USE_AUTOTUNE=0 (1.5Go GPU, not more than 3Go CPU). But it's still much more memory consumption than running process on CPU only. I tried a lot of thing like TF_GPU_ALLOCATOR=cuda_malloc_async with a tf nightly release, but it's still the same. This cause OOM errors if I would like to keep 10 processes on GPU like on CPU.
I found memory fragmentation may be a hint, by profiling a single process. You can find screenshots here.
TL;DR
When running tf process on CPU only, it uses some memory (comparable to data size). When running the same tf process on GPU only, it uses much more memory (~x16 without any tensorflow optimization).
I would like to know what can cause a huge difference of memory usage like this, and how to prevent it. Even how to fix it.
FYI -> Current setup : tf 2.6, cuda 11.4 (or 11.2 or 11.1 or 11.0), ubuntu 20.04, nvidia driver 370
EDIT : I tried to convert my tensorflow / tflearn code to pytorch. I have the same behaviour (low memory on CPU, and everything explode when running on GPU)
EDIT2 : Some of memory allocated on GPU should be for CUDA runtime. On pytorch. I have 300mo memory allocated on CPU run. I have 2go of GPU memory and almost 5go of CPU memory used when running on GPU. May the main problem is the CPU/system memory allocated for this process when I'm running on GPU, since it seems that CUDA runtime can take almost 2go of GPU mem (this is huge...). It looks like related to CUDA initialization.
EDIT3 : This is definitely an issue with CUDA. Even if I try to create a 1,1 tensor with pytorch, it takes 2go of GPU and almost 5go of CPU memory. It can be explain because pytorch is loading a huge number of kernels to memory; even if the main program isn't using them.
I see that you get better GPUs with the Pro account. But does switching to High-Ram affect the GPU's memory or the CPU's memory? I don't see it spelled out exactly anywhere, and maybe this is just common knowledge among ML experts.
Hi I'm working on developing an application, which involves working on shared data between GPU and CPU.
I know I can do atomic operation GPU and CPU separately. And also I don't want to use event synchronized between CPU and GPU.
Is there any way/command so that I can do atomic operation on shared data between CPU and integrated GPU in OpenCL?
It's possible but there are preconditions. You'll need a device supporting OpenCL 2.0 or higher (Intel, AMD and ARM all have such devices, i dunno about Nvidia).
To get started, look here,here and here.
When running a TensorFlow job I sometimes get a non-fatal error that says GPU memory exceeded, and then I see the "Shared memory GPU usage" go up on the Performance Monitor on Windows 10.
How does TensorFlow achieve this? I have looked at CUDA documentation and not found a reference to the Dedicated and Shared concepts used in the Performance Monitor. There is a Shared Memory concept in CUDA but I think it is something on the device, not the RAM I see in the Performance Monitor, which is allocated by the BIOS from CPU RAM.
Note: A similar question was asked but not answered by another poster.
Shared memory in windows 10 does not refer to the same concept as cuda shared memory (or local memory in opencl), it refers to host accessible/allocated memory from the GPU. For integrated graphics processing host and device memory is usually the same as shared thanks to both the cpu and gpu being located on the same die and being able to access the same ram. For dedicated graphics with their own memory, this is separate memory allocated on the host side for use by the GPU.
Shared memory for compute APIs such as through GLSL compute shaders, or Nvidia CUDA kernels refer to a programmer managed cache layer (some times refereed to as "scratch pad memory") which on Nvidia devices, exists per SM, and can only be accessed by a single SM and is usually between 32kB to 96kB per SM. Its purpose is to speed up memory access to data which is used often.
If you see and increase shared memory used in Tensorflow, you have a dedicated graphics card, and you are experiencing "GPU memory exceeded" it most likely means you are using too much memory on the GPU itself, so it is trying to allocate memory from elsewhere (IE from system RAM). This potentially can make your program much slower as the bandwidth and latency will be much worse on non device memory for a dedicated graphics card.
I think I figured this out by accident. The "Shared GPU Memory" reported by Windows 10 Task Manager Performance tab does get used, if there are multiple processes hitting the GPU simultaneously. I discovered this by writing a Python programming that used multiprocessing to queue up multiple GPU tasks, and I saw the "Shared GPU memory" start filling up. This is the only way I've seen it happen.
So it is only for queueing tasks. Each individual task is still limited to the onboard DRAM minus whatever is permanently allocated to actual graphics processing, which seems to be around 1GB.
My platform is Ubuntu running ob Exynos4412CPU which has the Mali400GPU. I would like to do some computer vision using OpenCV and OpenGL, I'm also going to do some fragment shaders. My question is what is the fastest way to copy the contents from the GPU to the CPU, which is really slow on my platform using glreadpixels. Is it beneficial to utilize glreadpixels in its own thread or use OpenMP ? Suggestions are welcome please :).
The Exynos 4412 doesn't have separate CPU and GPU memory at the hardware level; it's all the same RAM and physically accessible by both. Thus, there is likely to be some way to access the GPU's portion of the memory directly from the CPU.