(A Very) Experimental Threading in R
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
I’ve been trying to find a way to introduce threads to R. I guess there can be many reasons to do that, among which I could mention simplified input/output logic, sending tasks to the background (e.g. building a model asynchronously), running computation-intensive tasks in parallel (e.g. parallel, chunk-wise var()
on a large vector). Finally, it’s just a neat problem to look at I’m trying to follow approach similar to Python’s global interpreter lock.
So far it seems that:
- one can re-enter the interpreter with
R_tryEval
which internally callsR_ToplevelExec
, which in turn intercepts all long jumps (e.g. errors) - there are a few basic checks to verify whether the stack is in a good shape, e.g.
R_CStackStart
which checks stack frames andR_PPStackTop
which checks objects underPROTECT
ion
I think that one can run multiple threads in R and maintain a separate interpreter “instance” in each of them. R interpreter uses stack for its bookkeeping and each thread has its own stack. It also counts objects excluded from garbage collection with PROTECT
. Thus, when coming back to a given R interpreter “instance” (after thread-level context switch), one needs to pay attention to re-set R_PPStackTop
to whatever that thread was left with.
I have put these ideas together in the form of a R package thread (GitHub). This is what it can do:
- start a new thread and execute a R function in its own interpreter
- switch between threads on specific function calls, e.g.
thread_join()
,thread_print()
,thread_sleep()
- finish thread execution
- keep track of
R_PPStackTop
- avoid SIGSEGV-faulting the R process
Here’s an example where two functions are run in parallel R threads (it’s also available via thread::run_r_printing_example()
):
library(thread) thread_runner <- function (data) { thread_print(paste("thread", data, "starting\n")) for (i in 1:10) { timeout <- as.integer(abs(rnorm(1, 500, 1000))) thread_print(paste("thread", data, "iteration", i, "sleeping for", timeout, "\n")) thread_sleep(timeout) } thread_print(paste("thread", data, "exiting\n")) } message("starting the first thread") thread1 <- new_thread(thread_runner, 1) print(ls(threads)) message("starting the second thread") thread2 <- new_thread(thread_runner, 2) print(ls(threads)) message("going to join() both threads") thread_join(thread1) thread_join(thread2)
And here’s the output from my Ubuntu 16.10 x64:
starting the first thread [1] "thread_140737231587072" starting the second thread [1] "thread_140737223194368" "thread_140737231587072" going to join() both threads thread 1 starting thread 1 iteration 1 sleeping for 144 thread 2 starting thread 2 iteration 1 sleeping for 587 thread 1 iteration 2 sleeping for 761 thread 2 iteration 2 sleeping for 1327 thread 1 iteration 3 sleeping for 360 thread 1 iteration 4 sleeping for 1802 thread 2 iteration 3 sleeping for 704 thread 2 iteration 4 sleeping for 463 thread 1 iteration 5 sleeping for 368 thread 2 iteration 5 sleeping for 977 thread 1 iteration 6 sleeping for 261 thread 1 iteration 7 sleeping for 323 thread 1 iteration 8 sleeping for 571 thread 2 iteration 6 sleeping for 509 thread 2 iteration 7 sleeping for 2521 thread 1 iteration 9 sleeping for 298 thread 1 iteration 10 sleeping for 394 thread 1 exiting thread 2 iteration 8 sleeping for 966 thread 2 iteration 9 sleeping for 533 thread 2 iteration 10 sleeping for 1795 thread 2 exiting
How far is this from a real thread support in R? Well, there are three major challenges before this is really useful:
- Context switch happens only when a function from this package is called explicitly
- Memory allocation needs to be synchronized
- Error handling runs into
R_run_onexits
which in turn throws a very nasty error message – this suggests I haven’t covered all features of the interpreter related to switching stacks
Issues #1 and #2 are related: one cannot leave R (release R interpreter lock) and enter an arbitrary C function because it is legal to call allocVector()
from any C/C++ code. This in turn needs to happen synchronously – only one thread can execute allocVector()
(or more specifically, allocVector3()
) at any given time. I think that the best way to address it would be to patch R (main/memory.c
) and introduce a pointer to allocVector3
similar to ptr_R_WriteConsole
). Then the thread package would inject a decorator for allocVector3
with additional synchronization logic.
Issue #3 is not clear to me yet. But it also suggests more attention is needed to the specifics of R code execution.
I’ll be grateful for comments and suggestions. I think R could benefit from native thread support, if only to simplify program logic – but maybe also to run parts of computation-intensive code in lightweight parallel manner.
![](https://i0.wp.com/pixel.wp.com/b.gif?resize=1%2C1&ssl=1)
R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.