multi-level-parallelism {BGData}R Documentation

Multi-Level Parallelism


Functions with the nCores, i, and j parameters provide capabilities for both parallel and distributed computing.

For parallel computing, nCores determines the number of cores the code is run on. Memory usage can be an issue for higher values of nCores as R is not particularly memory-efficient. As a rule of thumb, at least around (nCores * object_size(chunk)) + object_size(result) MB of total memory will be needed for operations on file-backed matrices, not including potential copies of your data that might be created (for example lsfit runs cbind(1, X)). i and j can be used to include or exclude certain rows or columns. Internally, the mclapply function is used and therefore parallel computing will not work on Windows machines.

For distributed computing, i and j determine the subset of the input matrix that the code runs on. In an HPC environment, this can be used not just to include or exclude certain rows or columns, but also to partition the task among many nodes rather than cores. Scheduler-specific code and code to aggregate the results need to be written by the user. It is recommended to set nCores to 1 as nodes are often cheaper than cores.

See Also

mclapply to learn more about the function used to implement parallel computing. detectCores to detect the number of available cores.

[Package BGData version 2.4.0 Index]