Bath compression - #228
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| from .math.brents_root_finding import find_root_brents | ||
| from .math.krylov_exp import krylov_exp, DEFAULT_MAX_KRYLOV_DIM | ||
| from .jump_lindblad_operators import compute_noise_from_lindbladians | ||
| from .math.brents_root_finding import find_root_brents |
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I'm closing this, since it's made obsolete by #256 which creates a better improvement at lower runtime cost. |
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In this PR, I introduce a new method for packing Hermitian objects of the shape (χ, m , χ), specifically energy baths.
The main idea is to avoid storing the full Hermitian matrix. Instead, we store only the lower triangular part, including the main diagonal. The upper triangular part can then be reconstructed from the lower triangular entries using complex conjugation. Lower triangular columns are stacked to$$\chi(\chi+1)/2$$
mvectors of lengthThis reduces the memory footprint of an$$\chi \times \chi$$ matrix from $$\chi^2$$ to $$\chi(\chi+1)/2$$ .
The PR closes #95
(I've tried decompositions like
SVD,eigh,cholesky. No compression, baths are ~full rank tensors)the following part contains results for 5x5
quench_performance_gpufrom our benchmark suit.Memory measurement is done with
get_max_rssin emu_base/utils.py, which is usingtorch.cuda.max_memory_allocated(...). In the following I'm going to tests the new code with.memory_allocated(...)vs.max_memory_allocated(...)Results
torch.cuda.memory_allocated(...):Returns the current GPU memory occupied by tensors in bytes for a given device.torch.cuda.max_memory_allocated(...): <- MAX allocation:Return the maximum GPU memory occupied by tensors in bytes for a given device. By default, this returns the peak allocated memory since the beginning of this program.NB on performance.
every Hermitean bath tensor is validated on hermiticity. That can be weakened
Outcome
The real GPU memory occupation is reduced asymptotically by factor 2, though the peak memory occupation is still high and is not addressed here. The peak memory is hidden in operator application in Krylov routines