Comments (1)
Jutho
commented on May 18, 2024
Well, there will always be many cases where manual loops for specific contractions are faster than the general purpose algorithm used by tensorcontract. It is impossible to have code that is optimal for any given contraction, unless one generates code for every given contraction. With stagedfunction in julia v0.4, this would in principle be possible, but given the exponential increase in possible number of contractions, this might not be feasible. It's something I would like to try out once I have some more time.
I will anyway be updating TensorOperations.jl to make use of some of the new features of julia v0.4 soon, and hopefully this will bring along some performance improvements.
from tensoroperations.jl.
Related Issues (20)
- Sums are not commutative HOT 2
- Question: @cutensor not defined HOT 5
- possible memory leak with metaprogramming
- Why drop caching Tensors? HOT 3
- Is TensorOperations able to take advantage of symmetry in the output? HOT 8
- Manual allocation strategy HOT 2
- Floating Point Accuracy of @tensor results with CUDA HOT 3
- Enable multithreads when doing the permutedims in the TTGT algorithms HOT 2
- Unexpected `DimensionMismatch` (v4.0.2 -> v4.0.3) HOT 3
- Wrong result with subnetworks with equal labels HOT 2
- Bug in CUDA backend HOT 6
- Unintuitive `ncon` result when scalar HOT 2
- Taking gradients of traces HOT 6
- np.einsum_path vs TensorOperations HOT 3
- `ncon` fails with AD HOT 2
- `tensortrace` not working on Arrays of Symbolic Expressions from Symbolics.jl. HOT 2
- Combining LinearAlgebra.Diagonal with a CuArray inside @tensor HOT 2
- Compability with CUDA 5.2 HOT 3
- Confusion when using cuTENSOR HOT 4
- cuTENSOR not working with automatic differentiation HOT 5
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from tensoroperations.jl.