r-parallel

R Parallel & High Performance Skill

Sub-skills

Sub-skill	Description
r-parallel-local	parallel, future, furrr, foreach
r-parallel-distributed	sparklyr, batchtools, clustermq
r-parallel-cpp	Rcpp, RcppParallel, RcppArmadillo

Parallel computing and performance optimization in R.

Parallel Computing

Package	Description
future ★	Unified parallel/distributed API
furrr	future + purrr
foreach	Parallel loops
parallel	Built-in multicore/snow
doParallel	Parallel backend for foreach
Rmpi	MPI interface
batchtools	HPC cluster support

Distributed Computing

Package	Description
sparklyr ★	Spark interface (RStudio)
SparkR	Spark R frontend
DistributedR	HP Vertica platform
ddR	Distributed data structures

High Performance

Package	Description
Rcpp ★	C++ integration
cpp11	Header-only C++ interface
Rcpp11	C++11 Rcpp redesign
compiler	JIT compilation
data.table	Fast data manipulation

Quick Examples

# future - unified parallel API
library(future)
plan(multisession, workers = 4)  # Use 4 cores

# Async evaluation
f <- future({
  slow_computation(data)
})
result <- value(f)

# future.apply
library(future.apply)
results <- future_lapply(1:100, function(i) {
  expensive_function(i)
})

# furrr (future + purrr)
library(furrr)
plan(multisession, workers = 4)
results <- future_map(data_list, process_function)
results <- future_map_dfr(files, read_and_process)

# foreach
library(foreach)
library(doParallel)
registerDoParallel(cores = 4)

results <- foreach(i = 1:100, .combine = rbind) %dopar% {
  expensive_function(i)
}

# parallel (base R)
library(parallel)
cl <- makeCluster(4)
results <- parLapply(cl, 1:100, function(i) {
  expensive_function(i)
})
stopCluster(cl)

# Rcpp - C++ acceleration
library(Rcpp)
cppFunction('
  double sumC(NumericVector x) {
    int n = x.size();
    double total = 0;
    for(int i = 0; i < n; ++i) {
      total += x[i];
    }
    return total;
  }
')
sumC(1:1000000)  # Much faster than sum()

# Rcpp with sourceCpp
# save as mycode.cpp
# [[Rcpp::export]]
# double meanC(NumericVector x) {
#   return std::accumulate(x.begin(), x.end(), 0.0) / x.size();
# }
sourceCpp("mycode.cpp")

# Spark with sparklyr
library(sparklyr)
sc <- spark_connect(master = "local")
spark_df <- copy_to(sc, mtcars, "mtcars")

spark_df %>%
  group_by(cyl) %>%
  summarise(mean_mpg = mean(mpg)) %>%
  collect()

spark_disconnect(sc)