My question is actually simple: How can I manage to create several DoparParam() objects, each with a different number of registered cores? See below for what I tried. In a nutshell, it seems I can only have one DoparParam() object, because once I run registerDoParallel() existing objects will be overwritten in terms of number of registred cores.

Why is that important? Say I need one DoparParam with just very few cores, because the bplapply will be very memory-intensive, and one with many cores, because the bplapply will not be memory-intensive at all. How can I do that? Please do not suggest to use any of the other available backends. DoparParam (in my hands) works the smoothest, on all OS, with least problems, so I really would like to use that, but in a more flexible fashion in terms of having multiple ones available with different numbers of cores, and without running registerDoParallel before each bplapply, but rather once in a setup script, and then go along with the objects all the way through the project.

suppressMessages({

  library(BiocParallel)
  library(doParallel)

})

registerDoParallel(cores = 2)
bp2 <- DoparParam()
bp2
#> class: DoparParam
#>   bpisup: TRUE; bpnworkers: 2; bptasks: 0; bpjobname: BPJOB
#>   bplog: FALSE; bpthreshold: INFO; bpstopOnError: TRUE
#>   bpRNGseed: ; bptimeout: NA; bpprogressbar: FALSE
#>   bpexportglobals: TRUE; bpexportvariables: TRUE; bpforceGC: FALSE
#>   bpfallback: TRUE
#>   bplogdir: NA
#>   bpresultdir: NA

# no effect, still using 2 cores as workers
bp2$workers <- 18L
bp2
#> class: DoparParam
#>   bpisup: TRUE; bpnworkers: 2; bptasks: 0; bpjobname: BPJOB
#>   bplog: FALSE; bpthreshold: INFO; bpstopOnError: TRUE
#>   bpRNGseed: ; bptimeout: NA; bpprogressbar: FALSE
#>   bpexportglobals: TRUE; bpexportvariables: TRUE; bpforceGC: FALSE
#>   bpfallback: TRUE
#>   bplogdir: NA
#>   bpresultdir: NA

# no effect, still using 2 cores as workers
bptasks(bp2) <- 18L
bp2
#> class: DoparParam
#>   bpisup: TRUE; bpnworkers: 2; bptasks: 18; bpjobname: BPJOB
#>   bplog: FALSE; bpthreshold: INFO; bpstopOnError: TRUE
#>   bpRNGseed: ; bptimeout: NA; bpprogressbar: FALSE
#>   bpexportglobals: TRUE; bpexportvariables: TRUE; bpforceGC: FALSE
#>   bpfallback: TRUE
#>   bplogdir: NA
#>   bpresultdir: NA

# only valid for some Snow and MultiCore
bpworkers(bp2) <- 18L
#> Error in (function (classes, fdef, mtable) : unable to find an inherited method for function 'bpworkers<-' for signature '"DoparParam", "integer"'

# overwrites bp2
registerDoParallel(cores = 18)
bp18 <- DoparParam()
bp18
#> class: DoparParam
#>   bpisup: TRUE; bpnworkers: 18; bptasks: 0; bpjobname: BPJOB
#>   bplog: FALSE; bpthreshold: INFO; bpstopOnError: TRUE
#>   bpRNGseed: ; bptimeout: NA; bpprogressbar: FALSE
#>   bpexportglobals: TRUE; bpexportvariables: TRUE; bpforceGC: FALSE
#>   bpfallback: TRUE
#>   bplogdir: NA
#>   bpresultdir: NA
bp2
#> class: DoparParam
#>   bpisup: TRUE; bpnworkers: 18; bptasks: 18; bpjobname: BPJOB
#>   bplog: FALSE; bpthreshold: INFO; bpstopOnError: TRUE
#>   bpRNGseed: ; bptimeout: NA; bpprogressbar: FALSE
#>   bpexportglobals: TRUE; bpexportvariables: TRUE; bpforceGC: FALSE
#>   bpfallback: TRUE
#>   bplogdir: NA
#>   bpresultdir: NA

sessionInfo()
#> R version 4.3.2 (2023-10-31)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 22.04.3 LTS
#> 
#> Matrix products: default
#> BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0
#> 
#> locale:
#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
#>  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
#>  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
#>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
#>  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
#> 
#> time zone: Etc/UTC
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] parallel  stats     graphics  grDevices utils     datasets  methods  
#> [8] base     
#> 
#> other attached packages:
#> [1] doParallel_1.0.17   iterators_1.0.14    foreach_1.5.2      
#> [4] BiocParallel_1.36.0
#> 
#> loaded via a namespace (and not attached):
#>  [1] digest_0.6.33     codetools_0.2-19  fastmap_1.1.1     xfun_0.41        
#>  [5] glue_1.6.2        knitr_1.45        htmltools_0.5.7   rmarkdown_2.25   
#>  [9] lifecycle_1.0.4   cli_3.6.1         reprex_2.0.2      withr_2.5.2      
#> [13] compiler_4.3.2    rstudioapi_0.15.0 tools_4.3.2       evaluate_0.23    
#> [17] yaml_2.3.7        rlang_1.1.2       fs_1.6.3
Created on 2024-06-17 with reprex v2.0.2

I think the answer is "you can't". DoparParam() provides a 'wrapper' around doParallel functionality, e.g., bpworkers(DoparParam()) reads foreach::getDoParWorkers(), which references the most recently registered cluster.

The doParallel package is a CRAN package, not Bioconductor, so you could ask over on r-help@r-project.org, or maybe biostars.org or stackoverflow.com

You can register any number of parallel backends using BiocParallel though. See the vignette.