[Rd] parallel PSOCK connection latency is greater on Linux?
Gabriel Becker
g@bembecker @end|ng |rom gm@||@com
Tue Nov 2 01:55:45 CET 2021
Jeff,
Perhaps I'm just missing something here, but ms is generally milliseconds,
not microseconds (which are much smaller), right?
Also, this seems to just be how long it takes to roundtrip serialize iris
(in 4.1.0 on mac osx, as thats what I have handy right this moment):
> microbenchmark({x <- unserialize(serialize(iris, connection = NULL))})
Unit: microseconds
expr min lq
{ x <- unserialize(serialize(iris, connection = NULL)) } 35.378 36.0085
mean median uq max neval
40.26888 36.4345 43.641 80.39 100
> res <- system.time(replicate(10000, {x <- unserialize(serialize(iris,
connection = NULL))}))
> res/10000
user system elapsed
4.58e-05 2.90e-06 4.88e-05
Thus the overhead appears to be extremely minimal in your results above,
right? In fact it seems to be comparable or lower than replicate.
~G
On Mon, Nov 1, 2021 at 5:20 PM Jeff Keller <jeff using vtkellers.com> wrote:
> Hi Simon,
>
> I see there may have been some changes to address the TCP_NODELAY issue on
> Linux in
> https://github.com/wch/r-source/commit/82369f73fc297981e64cac8c9a696d05116f0797
> .
>
> I gave this a try with R 4.1.1, but I still see a 40ms compute floor. Am I
> misunderstanding these changes or how socketOptions is intended to be used?
>
> -Jeff
>
> library(parallel)
> library(microbenchmark)
> options(socketOptions = "no-delay")
> cl <- makeCluster(1)
> (x <- microbenchmark(clusterEvalQ(cl, iris), times = 100, unit = "us"))
> # Unit: microseconds
> # expr min lq mean median uq max
> neval
> # clusterEvalQ(cl, iris) 96.9 43986.73 40535.93 43999.59 44012.79 48046.6
> 100
>
> > On 11/04/2020 5:41 AM Iñaki Ucar <iucar using fedoraproject.org> wrote:
> >
> >
> > Please, check a tcpdump session on localhost while running the following
> script:
> >
> > library(parallel)
> > library(tictoc)
> > cl <- makeCluster(1)
> > Sys.sleep(1)
> >
> > for (i in 1:10) {
> > tic()
> > x <- clusterEvalQ(cl, iris)
> > toc()
> > }
> >
> > The initialization phase comprises 7 packets. Then, the 1-second sleep
> > will help you see where the evaluation starts. Each clusterEvalQ
> > generates 6 packets:
> >
> > 1. main -> worker PSH, ACK 1026 bytes
> > 2. worker -> main ACK 66 bytes
> > 3. worker -> main PSH, ACK 3758 bytes
> > 4. main -> worker ACK 66 bytes
> > 5. worker -> main PSH, ACK 2484 bytes
> > 6. main -> worker ACK 66 bytes
> >
> > The first two are the command and its ACK, the following are the data
> > back and their ACKs. In the first 4-5 iterations, I see no delay at
> > all. Then, in the following iterations, a 40 ms delay starts to happen
> > between packets 3 and 4, that is: the main process delays the ACK to
> > the first packet of the incoming result.
> >
> > So I'd say Nagle is hardly to blame for this. It would be interesting
> > to see how many packets are generated with TCP_NODELAY on. If there
> > are still 6 packets, then we are fine. If we suddenly see a gazillion
> > packets, then TCP_NODELAY does more harm than good. On the other hand,
> > TCP_QUICKACK would surely solve the issue without any drawback. As
> > Nagle himself put it once, "set TCP_QUICKACK. If you find a case where
> > that makes things worse, let me know."
> >
> > Iñaki
> >
> > On Wed, 4 Nov 2020 at 04:34, Simon Urbanek <simon.urbanek using r-project.org>
> wrote:
> > >
> > > I'm not sure the user would know ;). This is very system-specific
> issue just because the Linux network stack behaves so differently from
> other OSes (for purely historical reasons). That makes it hard to abstract
> as a "feature" for the R sockets that are supposed to be
> platform-independent. At least TCP_NODELAY is actually part of POSIX so it
> is on better footing, and disabling delayed ACK is practically only useful
> to work around the other side having Nagle on, so I would expect it to be
> rarely used.
> > >
> > > This is essentially RFC since we don't have a mechanism for socket
> options (well, almost, there is timeout and blocking already...) and I
> don't think we want to expose low-level details so perhaps one idea would
> be to add something like delay=NA to socketConnection() in order to not
> touch (NA), enable (TRUE) or disable (FALSE) TCP_NODELAY. I wonder if there
> is any other way we could infer the intention of the user to try to choose
> the right approach...
> > >
> > > Cheers,
> > > Simon
> > >
> > >
> > > > On Nov 3, 2020, at 02:28, Jeff <jeff using vtkellers.com> wrote:
> > > >
> > > > Could TCP_NODELAY and TCP_QUICKACK be exposed to the R user so that
> they might determine what is best for their potentially latency- or
> throughput-sensitive application?
> > > >
> > > > Best,
> > > > Jeff
> > > >
> > > > On Mon, Nov 2, 2020 at 14:05, Iñaki Ucar <iucar using fedoraproject.org>
> wrote:
> > > >> On Mon, 2 Nov 2020 at 02:22, Simon Urbanek <
> simon.urbanek using r-project.org> wrote:
> > > >>> It looks like R sockets on Linux could do with TCP_NODELAY --
> without (status quo):
> > > >> How many network packets are generated with and without it? If there
> > > >> are many small writes and thus setting TCP_NODELAY causes many small
> > > >> packets to be sent, it might make more sense to set TCP_QUICKACK
> > > >> instead.
> > > >> Iñaki
> > > >>> Unit: microseconds
> > > >>> expr min lq mean median
> uq max
> > > >>> clusterEvalQ(cl, iris) 1449.997 43991.99 43975.21 43997.1
> 44001.91 48027.83
> > > >>> neval
> > > >>> 1000
> > > >>> exactly the same machine + R but with TCP_NODELAY enabled in
> R_SockConnect():
> > > >>> Unit: microseconds
> > > >>> expr min lq mean median uq
> max neval
> > > >>> clusterEvalQ(cl, iris) 156.125 166.41 180.8806 170.247 174.298
> 5322.234 1000
> > > >>> Cheers,
> > > >>> Simon
> > > >>> > On 2/11/2020, at 3:39 AM, Jeff <jeff using vtkellers.com> wrote:
> > > >>> >
> > > >>> > I'm exploring latency overhead of parallel PSOCK workers and
> noticed that serializing/unserializing data back to the main R session is
> significantly slower on Linux than it is on Windows/MacOS with similar
> hardware. Is there a reason for this difference and is there a way to avoid
> the apparent additional Linux overhead?
> > > >>> >
> > > >>> > I attempted to isolate the behavior with a test that simply
> returns an existing object from the worker back to the main R session.
> > > >>> >
> > > >>> > library(parallel)
> > > >>> > library(microbenchmark)
> > > >>> > gcinfo(TRUE)
> > > >>> > cl <- makeCluster(1)
> > > >>> > (x <- microbenchmark(clusterEvalQ(cl, iris), times = 1000, unit
> = "us"))
> > > >>> > plot(x$time, ylab = "microseconds")
> > > >>> > head(x$time, n = 10)
> > > >>> >
> > > >>> > On Windows/MacOS, the test runs in 300-500 microseconds
> depending on hardware. A few of the 1000 runs are an order of magnitude
> slower but this can probably be attributed to garbage collection on the
> worker.
> > > >>> >
> > > >>> > On Linux, the first 5 or so executions run at comparable speeds
> but all subsequent executions are two orders of magnitude slower (~40
> milliseconds).
> > > >>> >
> > > >>> > I see this behavior across various platforms and hardware
> combinations:
> > > >>> >
> > > >>> > Ubuntu 18.04 (Intel Xeon Platinum 8259CL)
> > > >>> > Linux Mint 19.3 (AMD Ryzen 7 1800X)
> > > >>> > Linux Mint 20 (AMD Ryzen 7 3700X)
> > > >>> > Windows 10 (AMD Ryzen 7 4800H)
> > > >>> > MacOS 10.15.7 (Intel Core i7-8850H)
> > > >>> >
> > > >>> > ______________________________________________
> > > >>> > R-devel using r-project.org mailing list
> > > >>> > https://stat.ethz.ch/mailman/listinfo/r-devel
> > > >>> >
> > > >>> ______________________________________________
> > > >>> R-devel using r-project.org mailing list
> > > >>> https://stat.ethz.ch/mailman/listinfo/r-devel
> > > >> --
> > > >> Iñaki Úcar
> > > >
> > > > ______________________________________________
> > > > R-devel using r-project.org mailing list
> > > > https://stat.ethz.ch/mailman/listinfo/r-devel
> > > >
> > >
> >
> >
> > --
> > Iñaki Úcar
>
> ______________________________________________
> R-devel using r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-devel
>
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