TreeDist (version 2.14.1)

StartParallel: Calculate distances in parallel

Description

Accelerate distance calculation by employing multiple CPU workers.

Usage

StartParallel(...)
SetParallel(cl)
GetParallel(cl)
StopParallel(quietly = FALSE)

Value

StartParallel() and SetParallel() return the previous value of options("TreeDist-cluster").

GetParallel() returns the currently specified cluster.

StopParallel() returns TRUE if a cluster was destroyed, FALSE otherwise.

Arguments

...

Parameters to pass to makeCluster().

cl

An existing cluster.

quietly

Logical; if TRUE, do not warn when no cluster was running.

Author

Martin R. Smith ()

Details

OpenMP (recommended for all split-based metrics)

When the package is built with OpenMP support (the default on Linux and Windows; optional on macOS), all pairwise split-based distance calculations use an efficient multi-threaded batch path automatically — no cluster setup is required. The affected functions are:

• ClusteringInfoDistance() / MutualClusteringInfo()

• SharedPhylogeneticInfo() / DifferentPhylogeneticInfo()

• MatchingSplitInfo() / MatchingSplitInfoDistance()

• MatchingSplitDistance()

• InfoRobinsonFoulds()

• NyeSimilarity()

• JaccardRobinsonFoulds()

The number of OpenMP threads is controlled by the standard "mc.cores" option:

options(mc.cores = parallel::detectCores())  # use all available cores
options(mc.cores = 4L)                        # or a fixed number

The default is 1 (single-threaded).

R parallel cluster

StartParallel() creates an R socket cluster (via makeCluster()) and registers it for use by TreeDist. SetParallel() registers a pre-existing cluster. StopParallel() stops the cluster and releases resources.

When to use StartParallel(): for metrics that do not have an OpenMP batch path, namely tree-object-based distances such as NNIDist() and MASTSize() / MASTInfo(), or any function called via CompareAll(). R-cluster parallelism carries a serialisation overhead of ~2–3 s, so it is only beneficial for large problems.

When not to use StartParallel(): for the split-based metrics listed above. Registering a cluster disables the OpenMP batch path for those functions, replacing a thread-local C++ loop with inter-process communication — which is slower at every problem size measured. Call StopParallel() before computing split-based distances if a cluster is active.

Examples

# OpenMP parallelism: set mc.cores before calling any split-based metric.
options(mc.cores = 2L)
# MutualClusteringInfo(trees)  # uses 2 OpenMP threads automatically
options(mc.cores = NULL)  # restore default (single-threaded)

if (interactive()) {
  # R cluster: beneficial for NNIDist, MASTSize/MASTInfo, CompareAll(), etc.
  # Do NOT activate while computing split-based distances (MCI, SPI, MSI, …)
  # as it bypasses the faster OpenMP path.
  library("TreeTools", quietly = TRUE)
  nCores <- ceiling(parallel::detectCores() / 2)
  StartParallel(nCores) # Takes a few seconds to set up processes
  GetParallel()
  CompareAll(as.phylo(0:6, 100), NNIDist)
  StopParallel() # Returns system resources
}