CuPy only supports NVIDIA and AMD GPUs. I actually looked into supporting Apple's GPU via its Metal language. My conclusion was that it is not suitable for scientific computing (ex: it lacks double precision floating numbers and complex numbers) and it'd be very difficult for CuPy to work with.
@leofang @thomasaarholt Unless I'm missing something here, Metal at least as dgemm and the like: https://developer.apple.com/documentation/accelerate/blas
also has the z* functions
It's gotta be a full BLAS implementation
Hi, I'm trying to made the most of @fuse, but I can't find much documentation - in particular I'm struggling to find a way to use the size of array returned by one operation as a parameter for another eg:
cupy.random.random(size=(x.shape[0],1)). I would appreciate any advice. Thanks in advance.
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@nav-id didn't
x.size work for you?
Oh sorry
I take it back
you're asking about
@fuse đ
my apology, maybe someone from the team could give a better suggestion, I am less familiar with
@fuse's functionality
@leofang Ah, you're right. I did some searching though, and I found this "gemm" (bad pun): https://developer.apple.com/documentation/metalperformanceshaders/mpsmatrixmultiplication
look at alpha and beta
Not sure if the GPU is faster than the M1's dedicated hardware for linear algebra, but maybe a benchmark might tell
Im getting my greek letters and matrices mixed up
both facys
*Both facts from https://github.com/neurolabusc/Metal
If you are installing CuPy and cuTENSOR from conda-forge, we notice an binary incompatibility issue. For the time being, please limit the cuTENSOR version as follows:
conda install -c conda-forge cupy cutensor=1.2 ...We are working on a proper fix and will announce here once it is done.
@/all This is now fixed. conda install -c conda-forge cupy cutensor ... will work just fine (with cuTENSOR 1.2). The support of cuTENSOR 1.3 in conda-forge is ongoing.
@benvcutilli Yeah it looks odd. I think the ref you found is for "Metal Performance Shaders", which is kinda a collection of high-level constructs IIUC (consider it as the GPU version of a subset of Accelerate, or a mixed subset of cuDNN/cuSOLVER/etc), whereas the primitive type support that I was referring to is part of "Metal", the programming model (low-level, if you like), see the language guide https://developer.apple.com/metal/Metal-Shading-Language-Specification.pdf
so I'd rather compare "Metal" with "CUDA"
@benvcutilli Aha, the supported matrix/vector dtypes are listed here: https://developer.apple.com/documentation/metalperformanceshaders/mpsdatatype
so no double precision in Apple GPU
Right thats the thing I was looking at, but it didnt say "This is the list of types that are supported in matrix operations". And then float32 was in a different section with an ambiguous "Float" type that says it represents all floats or something like that, which doesnt make any sense.
3 replies
i think it boils down to the common complaint that apple's documentation is bad
:mega: Released CuPy v9.3.0 & v10.0.0b1!
This release includes support for CUDA 11.4 and Compute Capability 8.6 (RTX 30X0 and AX000 series),
Refer to the release notes for the full changes:
This release includes support for CUDA 11.4 and Compute Capability 8.6 (RTX 30X0 and AX000 series),
cupyx.scipy.sparse.linalg.* enhancements, and more!Refer to the release notes for the full changes:
hi guys, Iâve just started using cupy. what Iâm trying to do is to use cupy to replace numpy in my code. the problem is when I try to use http://cupy.dot() , it always report error, says âimplicit conversion to a host numpy array is not allowedâ. However, Iâve checked the document about http://copy.dot() and made sure I give http://cupy.dot() two cupy.ndarray as it requires. is there any possible explanation of this issue? The original numpy code can run normally. Thank you anywayđ„ș
:mega: Pre-Release (alpha/beta/RC) wheels will be removed from PyPI:
- Starting in v10.0.0b2, we stopped uploading pre-release binary wheels to PyPI. To try v10.0.0b2 wheels, you will need an extra options like
pip install cupy-cuda*** -f https://github.com/cupy/cupy/releases/tag/v10.0.0b2(see cupy/cupy#5671 for more info.) - We are also going to remove outdated (v8.0.0rc1 or earlier) pre-release binary wheels from PyPI on September 20th (see cupy/cupy#5667 for more info.)
Hi all ! I have a quick question: if this is not the best place for it, I'm happy to move it to the correct place :) Given
Aand B two ndarrays of sizes M, D, D and N, D, D, what would you recommend for efficiently computing cp.matmul(A[i], B[j]) for all i and j. I have already thought of looping through and expanding A[i] and multiplying by B. Any input would be welcome ! Thanks !
7 replies
Quick survey on dropping CUDA 10.1 support: https://twitter.com/CuPy_Team/status/1435465647178149895
