Comments (1)
maleadt
commented on June 4, 2024
1
This is the well-known issue of multiple array wrappers 'breaking' method dispatch, so I think this can be closed in favor of JuliaGPU/Adapt.jl#21.
And FWIW:
This will work in a REPL, but not running in VSCode (for some reason).
It doesn't 'work' in the REPL, it's just that we allow scalar iteration with a warning in an interactive session. It looks like VSCode isn't recognized as such, resulting in scalar iteration generating a hard error.
Since it has proven tricky to solve this from the Julia side (see the linked issue, which point to a couple of Base PRs that have stranded) I'm considering switching to unified memory by default such that scalar fallbacks at least perform somewhat decently, but they still wouldn't be executing on the GPU.
from cuda.jl.
Related Issues (20)
- NSight Compute: prevent API calls during precompilation HOT 1
- Track CuArray stream usage
- Integrated profiler: detect lack of permissions
- Dependencies in `profile.jl` constitute a significant fraction of the load time HOT 2
- Support for Julia 1.11
- Base.stack is underperforming. HOT 1
- Inverse Complex-to-Real FFT allocates GPU memory HOT 1
- Illegal memory access in GemmKernels on Julia 1.9
- cuDNN not available for your platform HOT 5
- Supporting precompilation without CUDA is problematic
- Power of adjoint of `CuSparseMatrix` does not work anymore HOT 2
- Cannot reset CuArray to zero HOT 2
- Inference failure with sort(::CuMatrix) after loading MLDatasets HOT 1
- Cannot take gradient of `sort` on 2D CuArray HOT 2
- Multi-threaded code hanging forever with Julia 1.10 HOT 11
- cuDNN: Provide wrappers for the declarative API HOT 3
- CUBLAS: nrm2 support for StridedCuArray with length requiring Int64 HOT 1
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from cuda.jl.