Comments (7)
Hi Vivian,
Though it sounds very useful, but I am afraid it would be a nightmare for us the developers: as you said there are non-thread-safe codes, so we would need to know in advance which one is or isn't (and that would conflict with letting the user use an arbitrary unknown-to-us python function as a likelihood).
Our approach is to compute each likelihood sequentially, and rely on the likelihood authors to take advantage of OpenMP palletisation themselves in the internal of the likelihood computation. If done right, this should result in a similar performance to computing the likelihoods in parallel on different threads.
Does this answer your question? Is there a specific setting that you are finding yourself into (e.g. having to evaluate tens of different likelihoods), so that we may be able to find an alternative way to speed it up?
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If CosmoLike does internal caching, then this should be solved by specifying speeds for individual parameters (as opposed to per-likelihood speeds), which is precisely #1
Keep an eye on it!
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The DES theory components are implemented in the DES code rather than in the theory at the moment because they make a bunch of non-general approximations (Limber, neglect RSD, etc..). It's a bit tricky in general to decide how to best split things up, but certainly agree this could be pulled out somehow.
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Hi both,
If e.g the P(k)-->gamma_t/w_theta/xi can be generalised and abstracted, we'd still need to implement some way to pipe more than one theory code (camb+that), which is in the pipeline anyway for 2.0.
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Related Issues (20)
- Importance minimization HOT 1
- Access to zstar from theories and likelihoods HOT 2
- `WantTensors: true` in `extra_args` results in a segfault (when also using `external_primordial_pk: true` HOT 2
- Cobaya icon in dark mode HOT 3
- Error installing likelihood data HOT 6
- `oversample_thin: true` does not seem to reduce output HOT 6
- Backward Compatibility with python 3.9 HOT 4
- "The sum of logpriors in the sample is not consistent." when resuming chains HOT 3
- Something went wrong when looking for a covmat HOT 1
- Script invocation is broken on Python 3.9 HOT 4
- Python invocation not doing anything HOT 4
- FutureWarning
- Interpolation error creating a delta chi2 = 20 on DESI likelihood HOT 7
- Installing DESI data: could not be found error HOT 4
- cobaya-install cannot name '__obsolete__' from 'cobaya' (unknown location) HOT 4
- cobaya-install planck_2018_highl_plik.TTTEEE fails HOT 2
- bao.generic likelihood not working for the distances of type Dv_over_rs HOT 6
- Error with the .yaml file generated from cosmo generator HOT 3
- *ERROR* Requested fast/slow separation, but all parameters have the same speed HOT 3
- NPIPE likelihood HOT 2
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