Parallel processing

The multi-threading in lasR is pretty similar to lidR, except it does not use the package future. Everything is natively coded in C++ with OpenMP. There are 4 different parallelization strategies that can be assigned for a given task with exec(pipeline, ncores = strategy_name(n)) or that can be assigned globally for the entiere session with set_parallel_strategy(strategy_name(n)).

lasR uses OpenMP which means that the package supports parallelism on Linux and Windows but not on macOS where Apple has explicitly disabled OpenMP support in compilers that they ship in Xcode. Interested readers can read the following links: OpenMP on macOS; OpenBLAS and OpenMP on macOS ; Enable OpenMP for macOS

Sequential strategy

set_parallel_strategy(sequential())
# or
exec(pipeline, on = f, ncores = sequential())

The sequential strategy is not the default strategy. However, it is easier to start with this option to explain some specificities of lasR. In sequential processing, as the name indicates, the files are processed sequentially, and nothing is parallelized. The point cloud from one file passes through the pipeline while the other files are waiting to be processed. This is represented in the figure below.

Concurrent points strategy

set_parallel_strategy(concurrent_points(4))
# or
exec(pipeline, on = f, ncores = concurrent_points(4))

Concurrent points with half_cores() is the default strategy. The files are processed sequentially. The point cloud from one file passes through the pipeline while the other files are waiting. Inside the pipeline, some stages are parallelized and are processing the points in different threads. Each core processes a subset of the point cloud. The stages that are parallelized are consequently faster, but in practice, not a lot of stages can easily be parallelized this way.

Concurrent files strategy

set_parallel_strategy(concurrent_files(4))
# or
exec(pipeline, on = f, ncores = concurrent_files(4))
# or
exec(pipeline, on = f, ncores = 4) # more convenient

The files are processed in parallel. The point cloud from several files passes through several cloned pipelines while the other files are waiting. Inside the pipeline, the stages are not parallelized. This puts a lot of pressure on the disk because many files are read simultaneously, but also each stage can write some raster/vector/LAS files simultaneously. Additionally, it uses a lot of memory since many LAS files are loaded in memory simultaneously. With modern and fast SSD disks and a significant amount of RAM, this is the fastest option. Of course, users should not use all their cores; otherwise, they may run out of memory. See also the benchmarks vignette.

Nested strategy

set_parallel_strategy(nested(4, 2))
# or
exec(pipeline, on = f, ncores = nested(4, 2))

The files are processed in parallel. The point cloud from several files passes through several cloned pipelines while the other files are waiting. Inside the pipeline, some stages are also parallelized and are processing the points in different threads. Nested is reserved for experts only.

Special cases

In lasR, everything is written in pure C++ except for two stages that inject user-defined R code and use the R C API (see R stages)

rasterize(20, user_function(Z))
callback(user_function(data))

R is NOT multi-threaded, and thus calling these stages in parallel is not thread-safe and will crash the R session in the best case or deeply corrupt the R memory in the worst case. Consequently, these stages are protected and pipelines involving these stages cannot be ran in parallel with the concurrent-files strategy.

Real timeline