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Home Page: https://github.com/jbrhm/CudaLibrary
License: MIT License
Cupybara is a library that aims to speed up GEMM in python using a CUDA backend.
Home Page: https://github.com/jbrhm/CudaLibrary
License: MIT License
I need to do some profiling to figure out why the first time running the code is so much slower
Add a testing suite to make sure that new changes do not break existing functionality
Certain deps aren't transferring over when installing.
The GPU provides worse performance for small matrices. Therefore, only alloc to the GPU if the matrices are large enough for there to be a performance boost for when parallelizing.
Fix the imports in the wheel
Fix Library Installation CMake
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