Comments (2)
mattwigway
commented on August 23, 2024
Parallelization is definitely possible with the new linear_hessian code introduced in #52. I think the right way to do it is to pre-calculate all steps and store them in shared memory, along with the utility, and then use a multiprocessing pool to chug through all the hessian results in parallel. The challenge is how to do this without having multiprocessing copy the whole MNLFullASC object.
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mattwigway
commented on August 23, 2024
Since Numpy is good about releasing the GIL, I can use threads and not have to deal with shared memory.
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Related Issues (20)
- ASCs slow to converge with some sample levels HOT 7
- Parameterized price-income transformations no longer work
- Should the Hessian exactly match statsmodels?
- Should ASCs be updated in Hessian calculation?
- Store indices in a mmapped file HOT 1
- Finding ASCs uses too much memory
- Why is there a precision problem in the Hessian test
- Why does the marked share error _increase_ in some iterations?
- are errors in second stage est handled right (I think they should be added to type shock)
- Multiple budget params
- Replace endogenous variables with patsy formulae
- Don't recompute constant part of utility in sorting HOT 1
- Use dask to compute utility HOT 1
- Multithreaded derivative calculation HOT 1
- Make sure exceptions in threads kill model fit
- Multidimensional derivative calculation
- Short circuit utility recomputation in sorting when there are no endogenous variables
- Endogenous variables cause supply/demand mismatch
- pytest doesn't work unless eqsormo is installed
- Use IIA property to calculate full Jacobian from diagonal HOT 1
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