I want to write an algorithm that would benefit from the GPU's superior hashing capability over the CPU.
Is PyOpenGL the answer? I don't want to use drawing tools, but simply run a "vanilla" python script ported to the GPU.
I have an ATI/AMD GPU if that means anything.
Is PyOpenGL the answer?
No. At least not in the way you expect it. If your GPU does support OpenGL-4.3 you could use Compute Shaders in OpenGL, but those are not written in Python
but simply run a "vanilla" python script ported to the GPU.
That's not how GPU computing works. You have to write the shaders of computation kernels in a special language. Either OpenCL or OpenGL Compute Shaders or, specific to NVIDIA, in CUDA.
Python would then just deliver the framework for getting the GPU computation running.
Related
I've been trying to improve the performance of my python scripts and would like to run some using my computer's built-in GPU. However, my computer is Windows 10 and its GPU is not CUDA compatible. From what I've seen, it seems that the GPU must be CUDA compatible in order for it to run python scripts. Is there any way to utilize my GPU for said purposes? If not, are there other programming languages in which I can do this?
The GPU is a proccessing unit for graphics. It most likely won't help except for drawing polygons, transfering data, or massive data sets. The closest you can get is importing a module (depending on your needs), that uses C++ to interact with the GPU (such as OpenCL), or coding interactions yourself (much more complicated).
To answer your 2nd question, C++ or C# should work with your GPU.
Please specify what script you are trying to run for more detail
Good luck!
I have written a piece of scientific code in python, mainly using the numpy library (especially Fast Fourier Transforms), and a bit of Cython. Nothing in CUDA or anything GPU related that I am aware of. There is no graphic interface, everything runs in the terminal (I'm using WSL2 on Windows). The whole code is mostly about number crunching, nothing fancy at all.
When I run my program, I see that CPU usage is ~ 100% (to be expected of course), but GPU usage also rises, to around 5%.
Is it possible that a part of the work gets offloaded automatically to the GPU? How else can I explain this small but consistent increase in GPU usage ?
Thanks for the help
No, there is no automatic offloading in Numpy, at least not with the standard Numpy implementation. Note that some specific FFT libraries can use the GPU, but the standard implementation of Numpy uses its own implementation of FFT called PocketFFT based on FFTPack that do not use the GPU. Cython do not perform any automatic implicit GPU offloading. The code need to do that explicitly/manually.
No GPU offloading are automatically performed because GPUs are not faster than CPUs for all tasks and offloading data to the GPU is expensive, especially with small arrays (due to the relatively high-latency of the PCI bus and kernel calls in such a case). Moreover, this is hard to do efficiently even in case where the GPUs could be theoretically faster.
The 5% GPU usage is relative to the frequency of the GPU which is often configured to use an adaptative frequency. For example my discrete Nv-1660S GPU frequency is currently 300 MHz while it can automatically reach 1.785 GHz. Using actually 5% of a GPU running at a 17% of its maximum frequency with a 2D rendering of a terminal is totally possible. On my machine, printing lines in a for loop at 10 FPS in a Windows terminal takes 6% of my GPU still running at low-frequency (0-1% without running anything).
If you want to check the frequency of your GPU and the load there are plenty of tools for that starting from vendor tools often installed with the driver to softwares like GPU-z on Windows. For Nvidia GPU, you can list the processes currently using you GPU with nvidia-smi (it should be rocm-smi on AMD GPUs).
Hello I know that the key to analyzing data and working with artificial intelligence is to use the gpu and not the cpu. The problem is that I don't know how to use it with Python in the visual studio code, I use Ubuntu, I already have nvidia installed
You have to use with the libraries that are designed to work with the GPUs.
You can use Numba to compile Python code directly to binary with CUDA/ROC support, but I don't really know how limiting it is.
Another way is to call APIs that are designed for parallel computing such as OpenCL, there is PyOpenCL binding for this.
A bit limiting, but sometimes valid way - OpenGL/DirectX compute shaders, they are extremely easy, but not so fast if you need to transfer data back and forth in small batches.
Today i was asking to me if is possible to do matrix calculation using gpu instead cpu because i know that a gpu is designed to do them faster then a cpu.
I searched on the net and i found notices about the matrix calculation using gpu with different python's libraries but my question is exists a documentation that descibes how should we write code to comunicate with a gpu.
I'm asking that because i want to develop my own one to better understand how gpu work and to try something different.
Thanks to all.
I solved that problem with OpenCL
OpenCL is a standard library that the vendor of the GPU's implement by their own. Like NVIDIA support openCL and other features thanks to CUDA library.
Here a good guide to get start
I have an Intel Graphics Card (Intel(R) HD Graphics 520, also am on Windows 10) and as far as I know I can't use CUDA unless I have a NVIDIA GPU. The purpose is to use Theano's GPU capabilities (for deep learning which is why I need GPU power).
Is there a workaround that somehow allows me to use CUDA with my current GPU?
If not is there another API that I can use with my current GPU for Theano (in Python 2.7)?
Or as a last option, using another language entirely, such as Java that has an API that allows for GPU use that I can use?
Figuring this out would be very helpful, because even though I just started with deep learning, I will probably get to the point where I need GPU parallel processing power to get results without waiting days at a minimum.
In order:
No. You must have a supported NVIDIA GPU to use CUDA.
As pointed out in comments, there is an alternative backend for Theano which uses OpenCL and which might work on your GPU
Intel support OpenCL on your GPU, so any language bindings for the OpenCL APIs, or libraries with in-built OpenCL would be a possible solution in this case
[This answer has been assembled from comments and added as a community wiki entry in order to get it off the unanswered queue for the CUDA tag].