Parallel processing tools — multithreading • lasR

lasR uses OpenMP to paralellize the internal C++ code. set_parallel_strategy() globally changes the strategy used to process the point clouds. sequential(), concurrent_files(), concurrent_points(), and nested() are functions to assign a parallelization strategy (see Details). has_omp_support() tells you if the lasR package was compiled with the support of OpenMP which is unlikely to be the case on MacOS.

Usage

set_parallel_strategy(strategy)

unset_parallel_strategy()

get_parallel_strategy()

ncores()

half_cores()

sequential()

concurrent_files(ncores = half_cores())

concurrent_points(ncores = half_cores())

nested(ncores = ncores()/4L, ncores2 = 2L)

has_omp_support()

Arguments

strategy: An object returned by one of sequential(), concurrent_points(), concurrent_files() or nested().
ncores: integer. Number of cores.
ncores2: integer. Number of cores. For nested strategy ncores is the number of concurrent files and ncores2 is the number of concurrent points.

Details

There are 4 strategies of parallel processing:

sequential: No parallelization at all: sequential()
concurrent-points: Point cloud files are processed sequentially one by one. Inside the pipeline, some stages are parallelized and are able to process multiple points simultaneously. Not all stages are natively parallelized. E.g. concurrent_points(4)
concurrent-files: Files are processed in parallel. Several files are loaded in memory and processed simultaneously. The entire pipeline is parallelized, but inside each stage, the points are processed sequentially. E.g. concurrent_files(4)
nested: Files are processed in parallel. Several files are loaded in memory and processed simultaneously, and inside some stages, the points are processed in parallel. E.g. nested(4,2)

concurrent-files is likely the most desirable and fastest option. However, it uses more memory because it loads multiple files. The default is concurrent_points(half_cores()) and can be changed globally using e.g. set_parallel_strategy(concurrent_files(4))

Examples

if (FALSE) { # \dontrun{
f <- paste0(system.file(package="lasR"), "/extdata/bcts/")
f <- list.files(f, pattern = "(?i)\\.la(s|z)$", full.names = TRUE)

pipeline <- reader_las() + rasterize(2, "imean")

ans <- exec(pipeline, on = f, progress = TRUE, ncores = concurrent_files(4))

set_parallel_strategy(concurrent_files(4))
ans <- exec(pipeline, on = f, progress = TRUE)
} # }