Hello Anna,
The speed of parallel computing depends on many factors. To avoid any
potential confounders, Please try to use this code for timing (assuming you
still have all the variables you used in your example)
```
parallel_param <- SnowParam(workers = ncores, type = "SOCK", tasks =
My motivation for using distributed memory was that my package is also
accessible on Windows. Is it better to use shared memory as default but
check the user's system and then switch to socket only if necessary?
Regarding the real data. I have 68 samples (rows) of methylation EPIC array
data (850K
Dear Anna,
According to the documentation of "BiocParallelParam", SnowParam() is a
subclass suitable for distributed memory (e.g. cluster) computing. If you're
running your code on a simpler machine with shared memory (e.g. your PC),
you're probably better off using MulticoreParam() instead. He
Dear Martin,
thank you very much for the quick response.
I will apply your advice and add the package to "Suggests".
Kind regards
Maren
--
NGS Integrative Genomics (NIG), Core Unit
Department of Human Genetics
University Medical Center G�
Hi,
On Tue, 8 Aug 2023 at 12:32, Sitte, Maren
wrote:
> Dear Bioconductor Developers,
>
>
> I received an email that my package "pwOmics" gets an error in the check
> under Linux.
> Install and build gets an OK, but check shows an error.
>
> I had a look and the problem seems to be:
>
> > Bio
Hi all!
I'm switching from the base R *parallel* package to *BiocParallel* for my
Bioconductor submission and I have two questions. First, I wanted advice on
whether I've implemented load balancing correctly. Second, I've noticed
that the running time is about 15% longer with BiocParallel. Any ide
Dear Bioconductor Developers,
I received an email that my package "pwOmics" gets an error in the check under
Linux.
Install and build gets an OK, but check shows an error.
I had a look and the problem seems to be:
> BiocGenerics:::testPackage("pwOmics")
Error in library("RUnit", quietly =
