Developer interface
Functions documented on this page are meant for developers wishing to
implement BPPARAM objects that extend the
BiocParallelParam virtual class to support additional parallel
back-ends.
## class extension
.prototype_update(prototype, ...)
## manager interface
.send_to(backend, node, value)
.recv_any(backend)
.send_all(backend, value)
.recv_all(backend)
## worker interface
.send(worker, value)
.recv(worker)
.close(worker)
## supporting implementations
.bpstart_impl(x)
.bpworker_impl(worker)
.bplapply_impl(X, FUN, ..., BPREDO = list(), BPPARAM = bpparam())
.bpiterate_impl(ITER, FUN, ..., REDUCE, init, reduce.in.order = FALSE,
BPPARAM = bpparam())
.bpstop_impl(x)prototype |
A named |
x |
A |
backend |
An object containing information about the cluster, returned by
|
worker |
The object to which the worker communicates via |
node |
An integer value indicating the node in the backend to which values are to be sent or received. |
value |
Any R object, to be sent to or from workers. |
X, ITER, FUN, REDUCE, init, reduce.in.order, BPREDO, BPPARAM |
See |
... |
For For |
Start a BPPARM implementation by creating a reference class, e.g.,
extending the virtual class BiocParallelParam. Because of
idiosyncracies in reference class field initialization, an instance of
the class should be created by calling the generator returned by
setRefClass() with a list of key-value pairs providing default
parameteter arguments. The default values for the
BiocParallelParam base class is provided in a list
.BiocParallelParam_prototype, and the function
.prototype_update() updates a prototype with new values,
typically provided by the user. See the example below.
BPPARAM implementations need to implement bpstart() and
bpstop() methods; they may also need to implement,
bplapply() and bpiterate() methods. Each method usually
performs implementation-specific functionality before calling the next
(BiocParallelParam) method. To avoid the intricacies of multiple
dispatch, the bodies of BiocParallelParam methods are available for
direct use as exported symbols.
bpstart,BiocParallelParam-method
(.bpstart_impl()) initiates logging, random number generation,
and registration of finalizers to ensure that started clusters are
stopped.
bpstop,BiocParallelParam-method (.bpstop_impl())
ensures appropriate clean-up of stopped clusters, including sending
the DONE semaphore. bpstart() will usually arrange for
workers to enter .bpworker_impl() to listen for and evaluate
tasks.
bplapply,ANY,BiocParallelParam-method and
bpiterate,ANY,BiocParallelParam-method
(.bplapply_impl(), .bpiterate_impl()) implement:
serial evaluation when there is a single core or task available;
BPREDO functionality, and parallel lapply-like or iterative
calculation.
Invoke .bpstart_impl(), .bpstop_impl(),
.bplapply_impl(), and .bpiterate_impl() after any
BPPARAM-specific implementation details.
New implementations will also implement bpisup() and
bpbackend() / bpbackend<-(); there are no default
methods.
The backends (object returned by bpbackend()) of new
BPPARAM implementations must support length() (number of
nodes). In addition, the backends must support .send_to() and
.recv_any() manager and .send(), .recv(), and
.close() worker methods. Default .send_all() and
.recv_all() methods are implemented as simple iterations along
the length(cluster), invoking .send_to() or
.recv_any() on each iteration.
The return value of .prototype_update() is a list with elements
in prototype substituted with key-value pairs provided in
....
All send* and recv* functions are endomorphic, returning a
cluster object.
##
## Extend BiocParallelParam; `.A()` is not meant for the end user
##
.A <- setRefClass(
"A",
contains = "BiocParallelParam",
fields = list(id = "character")
)
## Use a prototype for default values, including the prototype for
## inheritted fields
.A_prototype <- c(
list(id = "default_id"),
.BiocParallelParam_prototype
)
## Provide a constructor for the user
A <- function(...) {
prototype <- .prototype_update(.A_prototype, ...)
do.call(.A, prototype)
}
## Provide an R function for field access
bpid <- function(x)
x$id
## Create and use an instance, overwriting default values
bpid(A())
a <- A(id = "my_id", threshold = "WARN")
bpid(a)
bpthreshold(a)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.