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
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'm currently working on building a graphics library with Python. (I know opengl exists, but this is just for fun) Essentially, I want to be able to implement many of the common features available with opengl.
Right now, I'm using pyopencl which uses opencl under the hood to send code to the GPU for processing.
Here's the kicker though, I'm developing on a 2015 MacBook Pro that has an AMD GPU.
I've written all of the code to make this work and now I'm working on finding the bottlenecks in the code to try to optimize.
I've found that the biggest bottle neck is in my fragment shader implemenetation when I'm converting numpy arrays to cl.Buffer objects prior to GPU processing and then back to numpy arrays after processing.
Doing some research has led me to think that using SVMs would help to minimize this cost, but.... SVMs were introduced in opencl 2.0. Apparently, Apple has stopped supporting opencl in favor of their own in house GPU library Metal and so I'm stuck with opencl 1.2
So I guess my question is, has anyone else hit this roadblock and if so, what is the common way of handling it? If I transition to Metal, then my code is no longer as universal, but if I stay on opencl, I have performance problems. I could autodetect the platform that the code is being run on and use different implementations specific to the platform, but one of my problems is I don't know if there is a trusted standard implementation for Metal in Python.
I've seen a similar post on stackoverflow which tackles the problem in C++: Parallel implementation for multiple SVDs using CUDA
I want to do exactly the same in python, is that possible? I have multiple matrices (approximately 8000 with size 15x3) and each of them I want to decompose using the SVD. This takes years on a CPU. Is it possible to do that in python? My computer has an NVIDIA GPU installed. I already had a look at several libraries such as numba, pycuda, scikit-cuda, cupy but didnt found a way to implement my plan with that libraries. I would be very glad for some help.
cuPy gives access to cuSolver, including a batched SVD:
https://docs.cupy.dev/en/stable/reference/generated/cupy.linalg.svd.html
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].
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.