parallel number of cores according to memory?
Use an operating system that supports forking, like Linux or MacOSX, and use the parallel package mclapply function or similar to share memory for read operations. [1] And stop posting in HTML here. [1] https://cran.r-project.org/web/views/HighPerformanceComputing.html
On July 7, 2020 9:20:39 PM PDT, ivo welch <ivowel at gmail.com> wrote:
if I understand correctly, R makes a copy of the full environment for each process. thus, even if I have 32 processors, if I only have 64GB of RAM and my R process holds about 10GB, I should probably not spawn 32 processes. has anyone written a function that sets the number of cores for use (in mclapply) to be guessed at by appropriate memory requirements (e.g., "amount-of-RAM"/"RAM held by R")? (it would be even nicer if I could declare my 8GB data frame to be read-only and to be shared among my processes, but this is presumably technically very difficult.) pointers appreciated. /iaw [[alternative HTML version deleted]]
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