The **futurize** package allows you to easily turn sequential code into parallel code by piping the sequential code to the `futurize()` function. Easy! # TL;DR ```r library(futurize) plan(multisession) library(TSP) data("USCA50") tour <- solve_TSP(USCA50, method = "nn", rep = 10L) |> futurize() ``` # Introduction The **[TSP]** package provides algorithms for solving the traveling salesperson problem (TSP). ## Example: Example adopted from `help("solve_TSP", package = "TSP")`: ```r library(futurize) plan(multisession) library(TSP) data("USCA50") methods <- c( "identity", "random", "nearest_insertion", "cheapest_insertion", "farthest_insertion", "arbitrary_insertion", "nn", "repetitive_nn", "two_opt", "sa" ) ## calculate tours - each tour in parallel tours <- lapply(methods, FUN = function(m) { solve_TSP(USCA50, rep = 10L, method = m) |> futurize() }) names(tours) <- methods ``` This will parallelize the computations, given that we have set up parallel workers, e.g. ```r plan(multisession) ``` The built-in `multisession` backend parallelizes on your local computer and works on all operating systems. There are [other parallel backends] to choose from, including alternatives to parallelize locally as well as distributed across remote machines, e.g. ```r plan(future.mirai::mirai_multisession) ``` and ```r plan(future.batchtools::batchtools_slurm) ``` # Supported Functions The following **TSP** functions are supported by `futurize()`: * `solve_TSP()` # Without futurize: Manual PSOCK cluster setup For comparison, here is what it takes to parallelize `solve_TSP()` using the **parallel** and **doParallel** packages directly, without **futurize**: ```r library(TSP) library(parallel) library(doParallel) data("USCA50") ## Set up a PSOCK cluster and register it with foreach ncpus <- 4L cl <- makeCluster(ncpus) registerDoParallel(cl) ## Solve the TSP in parallel via foreach tour <- solve_TSP(USCA50, method = "nn", rep = 10L) ## Tear down the cluster stopCluster(cl) registerDoSEQ() ## reset foreach to sequential ``` This requires you to manually create a cluster, register it with **doParallel**, and remember to tear it down and reset the **foreach** backend when done. If you forget to call `stopCluster()`, or if your code errors out before reaching it, you leak background R processes. You also have to decide upfront how many CPUs to use and what cluster type to use. Switching to another parallel backend, e.g. a Slurm cluster, would require a completely different setup. With **futurize**, all of this is handled for you - just pipe to `futurize()` and control the backend with `plan()`. [TSP]: https://cran.r-project.org/package=TSP [other parallel backends]: https://www.futureverse.org/backends.html