#96 #125
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#96 #125
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TankredO
suggested changes
Apr 11, 2018
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| #### 3. Multi-core and cluster calculations | ||
| If you want to do your calculations on a cluster there are several ways to achieve it. |
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... run your calculations ...
| #### 3. Multi-core and cluster calculations | ||
| If you want to do your calculations on a cluster there are several ways to achieve it. | ||
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| In the first case you want to parallize n internal (not overall chains) on n cores. The argument "parallel = T" in "createBayesianSetup" allows only at most parallelization on 3 cores for the SMC, DEzs and DreamsSamplers. But by setting "parallel = n" to n cores in the "createBayesianSetup" and giving the settings list a "startValue" matrix with n rows, the internal chains of DEzs and DREAMzs will be parallelized on n cores. This works only for the DEzs and DREAMzs samplers. |
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... and passing/providing the settings list ...
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| ``` | ||
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| In the second case you want to parallize n internal chains on n cores with a external parallilzed likelihood function. Unlike the previous case, that way DEzs, DREAMzs, and SMC samplers can be paralllized. |
| likelihood <- function(X) sum(dnorm(c(1:10), mean = X, log = T)) | ||
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| ## Definition of the likelihood which will be calculated in parallel. Instead of the parApply function, we could also define a costly parallelized likelihood | ||
| Plikelihood <- function(param) parallel::parApply(cl = cl, X = param, MARGIN = 1, FUN = likelihood) |
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maybe better pLikelihood or PLikelihood or something like that
| out <- runMCMC(bayesianSetup, settings, sampler = "DEzs") | ||
| ``` | ||
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| In a another case your likelihood requires a costly model which is parallized. Start your cluster and export your model, the required libraries, and dlls. Now you can start your calculations with the argument "parallel = external" in createBayesianSetup. |
| ### Create Cluster with n Cores | ||
| cl <- parallel::makeCluster(n) | ||
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| ## export your model |
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Why are those commented out?
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Because it doesn't exist("complexModel")...all other examples can be run
| # return(ll) | ||
| } | ||
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| ## create BayesianSetup and settings |
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you are sometimes writing BayesianSetup and sometimes Bayesian Setup. Maybe unify this.
| cl <- parallel::makeCluster(n) | ||
| parallel::clusterEvalQ(cl, library(BayesianTools)) | ||
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| ## calculate parallel n Chains, for each Chain the likelihood will be calculated on one core |
| In the second case you want to parallize n internal chains on n cores with a external parallilzed likelihood function. Unlike the previous case, that way DEzs, DREAMzs, and SMC samplers can be paralllized. | ||
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| ```{r, eval = FALSE} | ||
| ### Create Cluster with n Cores |
| @@ -213,9 +213,103 @@ BS <- createBayesianSetup(likelihood, parallel = "external", ...) | |||
| runMCMC(BS, sampler = "SMC", ...) | |||
| ``` | |||
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| The in-build parallelization only allows the parallelization of the internal chains of SMC, DEzs and DREAMzs. | |||
TankredO
approved these changes
Apr 11, 2018
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I hope its clear now...
check also spelling and grammar