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
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
