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GPU acceleration #3
Description
gpu acceleration
Implement functionalities to Speed up the calculation
Task1 : matrix diagonalization
Accelerate the matrix diagonlization and have a single test file that :
- runs successfully with the numpy library
- runs successfully with the cupy library
- runs for a case for which the gpu demonstrates acceleration
Activity
biochem-fan commentedon Jul 25, 2023
I emailed this in January but to share it more widely:
If we want to calculate only at a single thickness, matrix diagonalization is less efficient and not necessary. We can directly calculate matrix exponentials.
Ultramicroscopy "Stacked-Bloch-wave electron diffraction simulations using GPU acceleration"
https://doi.org/10.1016/j.ultramic.2014.03.003
This calculates the matrix exponential directly, without diagonalization. The paper claims this approach is faster and more suitable for GPU. This is very interesting. Although the paper does not mention derivative calculation, it should also be possible.
The matrix exponential is available in SciPy as
scipy.linalg.expmand its derivative asscipy.linalg.expm_frechet. PyTorch and JAX also have theirs with GPU support.ronandrevon commentedon Jul 26, 2023
That sounds super interesting thank you for sharing this. We ll have a look at implementing it.
[-]Summer Internship[/-][+]GPU acceleration[/+]ronandrevon commentedon Mar 18, 2024
I have tested matrix diagonalization on a remote gpu and it appears that I was starting to get acceleration for 2000x2000 matrix diagonalization. So to get the blochwave code to run larger matrices like that you have 2 options :
Smaxso more beams in the vicinity of the ewald sphere are included in the matrixNmaxso larger beams may also be included in the eigen matrix.misc
You can make regular commits but ideally the new commits should leave the code in a working state.