Parallelism in R: (Extension)
R provides several ways to utilize parallelism, both implicitly and explicitly:
Implicit Parallelism:
The parallel package in R provides functions like mclapply() and parLapply() that can distribute computations across multiple cores or machines, without my need for explicit thread management.
Also, R's package library includes many packages that leverage parallelism under the hood, such as dplyr for data manipulation and ggplot2 for visualization, allowing users like me to benefit from parallelism without directly managing it.
Explicit Parallelism:
The parallel package also provides low-level functions like makeCluster() and parLapply() that allow developers to create and manage their own parallel computing environments, such as multi-core or multi-machine setups.
The foreach package, combined with the doParallel package, enables the use of parallel backends (e.g., multicore, cluster, GPU) for loop-based computations.
R's support for the Message Passing Interface (MPI) through packages like Rmpi and pbdMPI allows for distributed computing on high-performance computing (HPC) clusters.
Packages like future and furrr provide a unified interface for executing code in parallel, abstracting away the underlying parallelization mechanism.