lua_parallel {luajr}

Run Lua code in parallel

Description

Runs a Lua function multiple times, with function runs divided among multiple threads.

Usage

lua_parallel(func, n, threads, pre = NA_character_)

Arguments

func

Lua expression evaluating to a function.

n

Number of function executions.

threads

Number of threads to create, or a list of existing Lua states (e.g. as created by lua_open()), all different, one for each thread.

pre

Lua code block to run once for each thread at creation.

Details

This function is experimental. Its interface and behaviour are likely to change in subsequent versions of luajr.

lua_parallel() works as follows. A number threads of new Lua states is created with the standard Lua libraries and the luajr module opened in each (i.e. as though the states were created using lua_open()). Then, a thread is launched for each state. Within each thread, the code in pre is run in the corresponding Lua state. Then, func(i) is called for each i in 1:n, with the calls spread across the states. Finally, the Lua states are closed and the results are returned in a list.

Instead of an integer, threads can be a list of Lua states, e.g. NULL for the default Lua state or a state returned by lua_open(). This saves the time needed to open the new states, which takes a few milliseconds.

Value

List of n values returned from the Lua function func.

Safety and performance

Note that func has to be thread-safe. All pure Lua code and built-in Lua library functions are thread-safe, except for certain functions in the built-in os and io libraries (search for "thread safe" in the Lua 5.2 reference manual).

Additionally, use of luajr reference types is not thread-safe because these use R to allocate and manage memory, and R is not thread-safe. This means that you cannot safely use luajr.logical_r, luajr.integer_r, luajr.numeric_r, luajr.character_r, or other reference types within func. luajr.list and luajr.dataframe are fine, provided the list entries / dataframe columns are value types.

There is overhead associated with creating new Lua states and with gathering all the function results in an R list. It is advisable to check whether running your Lua code in parallel actually gives a substantial speed increase.

Examples

lua_parallel("function(i) return i end", n = 4, threads = 2)