Hi!
I am using DESeq2 on an in-house server which is shared among a handful of users and has not a cueing system. I want to set a limit on the number of nodes DESeq2 uses, for which I used MulticoreParams
as follows:
parallelParams <- MulticoreParam(workers = 4,progressbar = TRUE, log = TRUE)
register(parallelParams,default=TRUE)
parallelParams
Which prints the following:
class: MulticoreParam
bpisup: FALSE; bpnworkers: 4; bptasks: 2147483647; bpjobname: BPJOB
bplog: TRUE; bpthreshold: INFO; bpstopOnError: TRUE
bpRNGseed: ; bptimeout: 2592000; bpprogressbar: TRUE
bpexportglobals: TRUE; bpforceGC: TRUE
bplogdir: NA
bpresultdir: NA
cluster type: FORK
However, when I run DESeq2 and inspect the CPU work using htop
I see that it is using all of the available resources, not only the 4 nodes I asked for.
I have also tried the following mentioned on the DESeq2 vignette:
dds<-DESeq(dds,parallel=TRUE,BPPARAM=parallelParams)
but the result is the same.
I am running my code inside a singularity container pulled from docker biocontainers (tag 1.34.0--r41h399db7b_0). Running sessionInfo()
inside the container prints:
> sessionInfo()
R version 4.1.1 (2021-08-10)
Platform: x86_64-conda-linux-gnu (64-bit)
Running under: Debian GNU/Linux 10 (buster)
Matrix products: default
BLAS/LAPACK: /usr/local/lib/libopenblasp-r0.3.18.so
locale:
[1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C
[4] LC_COLLATE=C.UTF-8 LC_MONETARY=C LC_MESSAGES=C.UTF-8
[7] LC_PAPER=C LC_NAME=C LC_ADDRESS=C
[10] LC_TELEPHONE=C LC_MEASUREMENT=C LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
loaded via a namespace (and not attached):
[1] compiler_4.1.1
Thank you in advance,
Sebastian