Function to parallelize computation with various methods
parallel.compute(
vec,
func,
n.core = 2,
parallel.method = "mclapply",
count = TRUE
)List of the results for each element of `vec` argument.
Numeric vector including the values that are computed in parallel.
The function to be applied to each element of `vec` argument. This function must only have one argument.
Setting n.core > 1 will enable parallel execution on a machine with multiple cores. This argument is not valid when `parallel.method = "furrr"`.
Method for parallel computation. We offer three methods, "mclapply", "furrr", and "foreach".
When `parallel.method = "mclapply"`, we utilize pbmclapply function in the `pbmcapply` package
with `count = TRUE` and mclapply function in the `parallel` package with `count = FALSE`.
When `parallel.method = "furrr"`, we utilize future_map function in the `furrr` package.
With `count = TRUE`, we also utilize progressor function in the `progressr` package to show the progress bar,
so please install the `progressr` package from github (https://github.com/HenrikBengtsson/progressr).
For `parallel.method = "furrr"`, you can perform multi-thread parallelization by
sharing memories, which results in saving your memory, but quite slower compared to `parallel.method = "mclapply"`.
When `parallel.method = "foreach"`, we utilize foreach function in the `foreach` package
with the utilization of makeCluster function in `parallel` package,
and registerDoParallel function in `doParallel` package.
With `count = TRUE`, we also utilize setTxtProgressBar and
txtProgressBar functions in the `utils` package to show the progress bar.
We recommend that you use the option `parallel.method = "mclapply"`, but for Windows users, this parallelization method is not supported. So, if you are Windows user, we recommend that you use the option `parallel.method = "foreach"`.
When count is TRUE, you can know how far RGWAS has ended with percent display.