recv-method | R Documentation |
This method lets a rank receive (blocking) an object from the other rank in the same communicator. The default return is the object sent from the other rank.
recv(x.buffer = NULL, rank.source = .pbd_env$SPMD.CT$rank.source,
tag = .pbd_env$SPMD.CT$tag, comm = .pbd_env$SPMD.CT$comm,
status = .pbd_env$SPMD.CT$status,
check.type = .pbd_env$SPMD.CT$check.type)
x.buffer |
a buffer to store |
rank.source |
a source rank where |
tag |
a tag number. |
comm |
a communicator number. |
status |
a status number. |
check.type |
if checking data type first for handshaking. |
A corresponding send()
should be evoked at the corresponding rank
rank.source
.
These are high level S4 methods. By default, check.type
is TRUE
and an additional send()/recv()
will make a handshaking call first,
then deliver the data next.
i.e. an integer vector of length two (type and length)
will be deliver first between send()
and recv()
to ensure
a buffer (of right type and right size/length) is properly allocated at
the rank.dest
side.
Currently, four data types are considered: integer
, double
,
raw/byte
, and default/raw.object
. The default method will
make a serialize()
call first to convert the general R object into
a raw
vector before sending it away. After the raw
vector
is received at the rank.dest
side, the vector will be
unserialize()
back to the R object format.
check.type
set as FALSE
will stop the additional
handhsaking call, but the buffer should be prepared carefully
by the user self. This is typically for the advanced users and
more specifically calls are needed. i.e. calling those
spmd.send.integer
with spmd.recv.integer
correspondingly.
check.type
also needs to be set as FALSE
for more efficient
calls such as isend()/recv()
or send()/irecv()
. Currently,
no check types are implemented in those mixed calls.
An object is returned by default and the buffer will be overwritten implicitely.
For calling spmd.recv.*()
:
signature(x = "ANY")
signature(x = "integer")
signature(x = "numeric")
signature(x = "raw")
Wei-Chen Chen wccsnow@gmail.com, George Ostrouchov, Drew Schmidt, Pragneshkumar Patel, and Hao Yu.
Programming with Big Data in R Website: https://pbdr.org/
irecv()
, send()
, isend()
.
### Save code in a file "demo.r" and run with 2 processors by
### SHELL> mpiexec -np 2 Rscript demo.r
spmd.code <- "
### Initialize
suppressMessages(library(pbdMPI, quietly = TRUE))
.comm.size <- comm.size()
.comm.rank <- comm.rank()
### Examples.
N <- 5
x <- (1:N) + N * .comm.rank
if(.comm.rank == 0){
y <- send(matrix(x, nrow = 1))
} else if(.comm.rank == 1){
y <- recv()
}
comm.print(y, rank.print = 1)
### Finish.
finalize()
"
pbdMPI::execmpi(spmd.code, nranks = 2L)
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