Comments (1)
CTF treats vectors no different from tensors, and the code has to account for generalized redistribution and storage format. We did use specialized code to handle vectors without overheads in a different project, and found the performance gains to be quite significant. We will integrate this into CTF soon, and tag here when it is done.
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Related Issues (20)
- Efficiency comparisons with einsum and opt-einsum and how to utilize symmetry? HOT 4
- ctf-einsum.py:78: DeprecationWarning: `np.complex` is a deprecated alias for the builtin `complex`. HOT 1
- issues using sparse file io to load tensors HOT 4
- Compile error with GCC 11 HOT 10
- Wrong results when slicing a symmetric sparse tensor in python lib
- understanding performance overheads in CTF HOT 27
- ModuleNotFoundError: No module named 'ctf.core' HOT 1
- Comparing Fortran and CTF performance on symmetries in tensor contractions HOT 2
- segfault executing sparse inner product HOT 18
- oom/memory corruption running an SDDMM (using TTTP specialized routine) HOT 1
- [question] setting all nonzeros to a value HOT 2
- Warnings
- make test failure in SVD test HOT 3
- compiling issue when including ctf.hpp
- test_suite failure on Apple HOT 1
- compile issues with undefined references to mkl commands (that appear in the relevant folders) HOT 9
- Scale with Endomorphism
- Set values with low memory footprint
- ctf header error during installation on perlmutter HOT 2
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