Distributed Computing
- Cluster Manager Interface

Distributed Computing

addprocs(manager::ClusterManager; kwargs...) -> List of process identifiers

Launches worker processes via the specified cluster manager.

For example, Beowulf clusters are supported via a custom cluster manager implemented in the package ClusterManagers.jl.

The number of seconds a newly launched worker waits for connection establishment from the master can be specified via variable JULIA_WORKER_TIMEOUT in the worker process’s environment. Relevant only when using TCP/IP as transport.

To launch workers without blocking the REPL, or the containing function if launching workers programmatically, execute addprocs in its own task.

Examples

# On busy clusters, call `addprocs` asynchronously
t = @async addprocs(...)

# Utilize workers as and when they come online
if nprocs() > 1   # Ensure at least one new worker is available
   ....   # perform distributed execution
end

# Retrieve newly launched worker IDs, or any error messages
if istaskdone(t)   # Check if `addprocs` has completed to ensure `fetch` doesn't block
    if nworkers() == N
        new_pids = fetch(t)
    else
        fetch(t)
    end
end

addprocs(machines; tunnel=false, sshflags=``, max_parallel=10, kwargs...) -> List of process identifiers

Add processes on remote machines via SSH. See exename to set the path to the julia installation on remote machines.

machines is a vector of machine specifications. Workers are started for each specification.

A machine specification is either a string machine_spec or a tuple - (machine_spec, count).

machine_spec is a string of the form [user@]host[:port] [bind_addr[:port]]. user defaults to current user, port to the standard ssh port. If [bind_addr[:port]] is specified, other workers will connect to this worker at the specified bind_addr and port.

count is the number of workers to be launched on the specified host. If specified as :auto it will launch as many workers as the number of CPU threads on the specific host.

Keyword arguments:

tunnel: if true then SSH tunneling will be used to connect to the worker from the master process. Default is false.
multiplex: if true then SSH multiplexing is used for SSH tunneling. Default is false.
ssh: the name or path of the SSH client executable used to start the workers. Default is "ssh".
sshflags: specifies additional ssh options, e.g. sshflags=`-i /home/foo/bar.pem`
max_parallel: specifies the maximum number of workers connected to in parallel at a host. Defaults to 10.
shell: specifies the type of shell to which ssh connects on the workers.
- shell=:posix: a POSIX-compatible Unix/Linux shell (sh, ksh, bash, dash, zsh, etc.). The default.
- shell=:csh: a Unix C shell (csh, tcsh).
- shell=:wincmd: Microsoft Windows cmd.exe.
dir: specifies the working directory on the workers. Defaults to the host’s current directory (as found by pwd())
enable_threaded_blas: if true then BLAS will run on multiple threads in added processes. Default is false.
exename: name of the julia executable. Defaults to "$(Sys.BINDIR)/julia" or "$(Sys.BINDIR)/julia-debug" as the case may be.
exeflags: additional flags passed to the worker processes.
topology: Specifies how the workers connect to each other. Sending a message between unconnected workers results in an error.
- topology=:all_to_all: All processes are connected to each other. The default.
- topology=:master_worker: Only the driver process, i.e. pid 1 connects to the workers. The workers do not connect to each other.
- topology=:custom: The launch method of the cluster manager specifies the connection topology via fields ident and connect_idents in WorkerConfig. A worker with a cluster manager identity ident will connect to all workers specified in connect_idents.
lazy: Applicable only with topology=:all_to_all. If true, worker-worker connections are setup lazily, i.e. they are setup at the first instance of a remote call between workers. Default is true.
env: provide an array of string pairs such as env=["JULIA_DEPOT_PATH"=>"/depot"] to request that environment variables are set on the remote machine. By default only the environment variable JULIA_WORKER_TIMEOUT is passed automatically from the local to the remote environment.
cmdline_cookie: pass the authentication cookie via the --worker commandline option. The (more secure) default behaviour of passing the cookie via ssh stdio may hang with Windows workers that use older (pre-ConPTY) Julia or Windows versions, in which case cmdline_cookie=true offers a work-around.

Julia 1.6

The keyword arguments ssh, shell, env and cmdline_cookie were added in Julia 1.6.

Environment variables:

If the master process fails to establish a connection with a newly launched worker within 60.0 seconds, the worker treats it as a fatal situation and terminates. This timeout can be controlled via environment variable JULIA_WORKER_TIMEOUT. The value of JULIA_WORKER_TIMEOUT on the master process specifies the number of seconds a newly launched worker waits for connection establishment.

addprocs(; kwargs...) -> List of process identifiers

Equivalent to addprocs(Sys.CPU_THREADS; kwargs...)

Note that workers do not run a .julia/config/startup.jl startup script, nor do they synchronize their global state (such as global variables, new method definitions, and loaded modules) with any of the other running processes.

addprocs(np::Integer; restrict=true, kwargs...) -> List of process identifiers

Launches workers using the in-built LocalManager which only launches workers on the local host. This can be used to take advantage of multiple cores. addprocs(4) will add 4 processes on the local machine. If restrict is true, binding is restricted to 127.0.0.1. Keyword args dir, exename, exeflags, topology, lazy and enable_threaded_blas have the same effect as documented for addprocs(machines).

Distributed.nprocs — Function

nprocs()

Get the number of available processes.

Examples

julia> nprocs()
3
julia> workers()
2-element Array{Int64,1}:
 2
 3

Distributed.nworkers — Function

nworkers()

Get the number of available worker processes. This is one less than nprocs(). Equal to nprocs() if nprocs() == 1.

Examples

$ julia -p 2
julia> nprocs()
3
julia> nworkers()
2

Distributed.procs — Method

procs()

Return a list of all process identifiers, including pid 1 (which is not included by workers()).

Examples

$ julia -p 2
julia> procs()
3-element Array{Int64,1}:
 1
 2
 3

Distributed.procs — Method

procs(pid::Integer)

Return a list of all process identifiers on the same physical node. Specifically all workers bound to the same ip-address as pid are returned.

Distributed.workers — Function

workers()

Return a list of all worker process identifiers.

Examples

$ julia -p 2
julia> workers()
2-element Array{Int64,1}:
 2
 3

Distributed.rmprocs — Function

rmprocs(pids...; waitfor=typemax(Int))

Remove the specified workers. Note that only process 1 can add or remove workers.

Argument waitfor specifies how long to wait for the workers to shut down:

If unspecified, rmprocs will wait until all requested pids are removed.
An ErrorException is raised if all workers cannot be terminated before the requested waitfor seconds.
With a waitfor value of 0, the call returns immediately with the workers scheduled for removal in a different task. The scheduled Task object is returned. The user should call wait on the task before invoking any other parallel calls.

Examples

$ julia -p 5
julia> t = rmprocs(2, 3, waitfor=0)
Task (runnable) @0x0000000107c718d0
julia> wait(t)
julia> workers()
3-element Array{Int64,1}:
 4
 5
 6

Distributed.interrupt — Function

interrupt(pids::Integer...)

Interrupt the current executing task on the specified workers. This is equivalent to pressing Ctrl-C on the local machine. If no arguments are given, all workers are interrupted.

interrupt(pids::AbstractVector=workers())

Interrupt the current executing task on the specified workers. This is equivalent to pressing Ctrl-C on the local machine. If no arguments are given, all workers are interrupted.

Distributed.myid — Function

myid()

Get the id of the current process.

Examples

julia> myid()
1
julia> remotecall_fetch(() -> myid(), 4)
4

Distributed.pmap — Function

pmap(f, [::AbstractWorkerPool], c...; distributed=true, batch_size=1, on_error=nothing, retry_delays=[], retry_check=nothing) -> collection

Transform collection c by applying f to each element using available workers and tasks.

For multiple collection arguments, apply f elementwise.

Note that f must be made available to all worker processes; see Code Availability and Loading Packages for details.

If a worker pool is not specified, all available workers, i.e., the default worker pool is used.

By default, pmap distributes the computation over all specified workers. To use only the local process and distribute over tasks, specify distributed=false. This is equivalent to using asyncmap. For example, pmap(f, c; distributed=false) is equivalent to asyncmap(f,c; ntasks=()->nworkers())

pmap can also use a mix of processes and tasks via the batch_size argument. For batch sizes greater than 1, the collection is processed in multiple batches, each of length batch_size or less. A batch is sent as a single request to a free worker, where a local asyncmap processes elements from the batch using multiple concurrent tasks.

Any error stops pmap from processing the remainder of the collection. To override this behavior you can specify an error handling function via argument on_error which takes in a single argument, i.e., the exception. The function can stop the processing by rethrowing the error, or, to continue, return any value which is then returned inline with the results to the caller.

Consider the following two examples. The first one returns the exception object inline, the second a 0 in place of any exception:

julia> pmap(x->iseven(x) ? error("foo") : x, 1:4; on_error=identity)
4-element Array{Any,1}:
 1
  ErrorException("foo")
 3
  ErrorException("foo")
julia> pmap(x->iseven(x) ? error("foo") : x, 1:4; on_error=ex->0)
4-element Array{Int64,1}:
 1
 0
 3
 0

Errors can also be handled by retrying failed computations. Keyword arguments retry_delays and retry_check are passed through to retry as keyword arguments delays and check respectively. If batching is specified, and an entire batch fails, all items in the batch are retried.

Note that if both on_error and retry_delays are specified, the on_error hook is called before retrying. If on_error does not throw (or rethrow) an exception, the element will not be retried.

Example: On errors, retry f on an element a maximum of 3 times without any delay between retries.

pmap(f, c; retry_delays = zeros(3))

Example: Retry f only if the exception is not of type InexactError, with exponentially increasing delays up to 3 times. Return a NaN in place for all InexactError occurrences.

pmap(f, c; on_error = e->(isa(e, InexactError) ? NaN : rethrow()), retry_delays = ExponentialBackOff(n = 3))

Distributed.RemoteException — Type

RemoteException(captured)

Exceptions on remote computations are captured and rethrown locally. A RemoteException wraps the pid of the worker and a captured exception. A CapturedException captures the remote exception and a serializable form of the call stack when the exception was raised.

Distributed.Future — Type

Future(w::Int, rrid::RRID, v::Union{Some, Nothing}=nothing)

A Future is a placeholder for a single computation of unknown termination status and time. For multiple potential computations, see RemoteChannel. See remoteref_id for identifying an AbstractRemoteRef.

Distributed.RemoteChannel — Type

RemoteChannel(pid::Integer=myid())

Make a reference to a Channel{Any}(1) on process pid. The default pid is the current process.

RemoteChannel(f::Function, pid::Integer=myid())

Create references to remote channels of a specific size and type. f is a function that when executed on pid must return an implementation of an AbstractChannel.

For example, RemoteChannel(()->Channel{Int}(10), pid), will return a reference to a channel of type Int and size 10 on pid.

The default pid is the current process.

Base.fetch — Method

fetch(x::Future)

Wait for and get the value of a Future. The fetched value is cached locally. Further calls to fetch on the same reference return the cached value. If the remote value is an exception, throws a RemoteException which captures the remote exception and backtrace.

Base.fetch — Method

fetch(c::RemoteChannel)

Wait for and get a value from a RemoteChannel. Exceptions raised are the same as for a Future. Does not remove the item fetched.

Distributed.remotecall — Method

remotecall(f, id::Integer, args...; kwargs...) -> Future

Call a function f asynchronously on the given arguments on the specified process. Return a Future. Keyword arguments, if any, are passed through to f.

Distributed.remotecall_wait — Method

remotecall_wait(f, id::Integer, args...; kwargs...)

Perform a faster wait(remotecall(...)) in one message on the Worker specified by worker id id. Keyword arguments, if any, are passed through to f.

See also fetch and remotecall.

Examples

$ julia -p 2
julia> remotecall_fetch(sqrt, 2, 4)
2.0
julia> remotecall_fetch(sqrt, 2, -4)
ERROR: On worker 2:
DomainError with -4.0:
sqrt will only return a complex result if called with a complex argument. Try sqrt(Complex(x)).
...

Distributed.remote_do — Method

remote_do(f, id::Integer, args...; kwargs...) -> nothing

Executes f on worker id asynchronously. Unlike remotecall, it does not store the result of computation, nor is there a way to wait for its completion.

A successful invocation indicates that the request has been accepted for execution on the remote node.

While consecutive remotecalls to the same worker are serialized in the order they are invoked, the order of executions on the remote worker is undetermined. For example, remote_do(f1, 2); remotecall(f2, 2); remote_do(f3, 2) will serialize the call to f1, followed by f2 and f3 in that order. However, it is not guaranteed that f1 is executed before f3 on worker 2.

Any exceptions thrown by f are printed to stderr on the remote worker.

Keyword arguments, if any, are passed through to f.

Base.put! — Method

put!(rr::RemoteChannel, args...)

Store a set of values to the RemoteChannel. If the channel is full, blocks until space is available. Return the first argument.

Base.put! — Method

put!(rr::Future, v)

Store a value to a Future rr. Futures are write-once remote references. A put! on an already set Future throws an Exception. All asynchronous remote calls return Futures and set the value to the return value of the call upon completion.

Base.take! — Method

take!(rr::RemoteChannel, args...)

Fetch value(s) from a RemoteChannel rr, removing the value(s) in the process.

Base.isready — Method

isready(rr::RemoteChannel, args...)

Determine whether a RemoteChannel has a value stored to it. Note that this function can cause race conditions, since by the time you receive its result it may no longer be true. However, it can be safely used on a Future since they are assigned only once.

Base.isready — Method

isready(rr::Future)

Determine whether a Future has a value stored to it.

If the argument Future is owned by a different node, this call will block to wait for the answer. It is recommended to wait for rr in a separate task instead or to use a local Channel as a proxy:

p = 1
f = Future(p)
errormonitor(@async put!(f, remotecall_fetch(long_computation, p)))
isready(f)  # will not block

Distributed.AbstractWorkerPool — Type

AbstractWorkerPool

Supertype for worker pools such as WorkerPool and CachingPool. An AbstractWorkerPool should implement:

push! - add a new worker to the overall pool (available + busy)
put! - put back a worker to the available pool
take! - take a worker from the available pool (to be used for remote function execution)
length - number of workers available in the overall pool
isready - return false if a take! on the pool would block, else true

The default implementations of the above (on a AbstractWorkerPool) require fields

channel::Channel{Int}
workers::Set{Int}

where channel contains free worker pids and workers is the set of all workers associated with this pool.

Distributed.WorkerPool — Type

WorkerPool(workers::Vector{Int})

Create a WorkerPool from a vector of worker ids.

Examples

$ julia -p 3
julia> WorkerPool([2, 3])
WorkerPool(Channel{Int64}(sz_max:9223372036854775807,sz_curr:2), Set([2, 3]), RemoteChannel{Channel{Any}}(1, 1, 6))

Distributed.CachingPool — Type

CachingPool(workers::Vector{Int})

An implementation of an AbstractWorkerPool. remote, remotecall_fetch, pmap (and other remote calls which execute functions remotely) benefit from caching the serialized/deserialized functions on the worker nodes, especially closures (which may capture large amounts of data).

The remote cache is maintained for the lifetime of the returned CachingPool object. To clear the cache earlier, use clear!(pool).

For global variables, only the bindings are captured in a closure, not the data. let blocks can be used to capture global data.

Examples

const foo = rand(10^8);
wp = CachingPool(workers())
let foo = foo
    pmap(i -> sum(foo) + i, wp, 1:100);
end

The above would transfer foo only once to each worker.

Distributed.default_worker_pool — Function

default_worker_pool()

WorkerPool containing idle workers - used by remote(f) and pmap (by default).

Examples

$ julia -p 3
julia> default_worker_pool()
WorkerPool(Channel{Int64}(sz_max:9223372036854775807,sz_curr:3), Set([4, 2, 3]), RemoteChannel{Channel{Any}}(1, 1, 4))

Distributed.clear! — Method

clear!(pool::CachingPool) -> pool

Removes all cached functions from all participating workers.

Distributed.remote — Function

remote([p::AbstractWorkerPool], f) -> Function

Return an anonymous function that executes function f on an available worker (drawn from WorkerPool p if provided) using remotecall_fetch.

Distributed.remotecall — Method

remotecall(f, pool::AbstractWorkerPool, args...; kwargs...) -> Future

WorkerPool variant of remotecall(f, pid, ....). Wait for and take a free worker from pool and perform a remotecall on it.

Examples

$ julia -p 3
julia> wp = WorkerPool([2, 3]);
julia> A = rand(3000);
julia> f = remotecall(maximum, wp, A)
Future(2, 1, 6, nothing)

In this example, the task ran on pid 2, called from pid 1.

Distributed.remotecall_wait — Method

remotecall_wait(f, pool::AbstractWorkerPool, args...; kwargs...) -> Future

WorkerPool variant of remotecall_wait(f, pid, ....). Wait for and take a free worker from pool and perform a remotecall_wait on it.

Examples

$ julia -p 3
julia> wp = WorkerPool([2, 3]);
julia> A = rand(3000);
julia> f = remotecall_wait(maximum, wp, A)
Future(3, 1, 9, nothing)
julia> fetch(f)
0.9995177101692958

Distributed.remotecall_fetch — Method

remotecall_fetch(f, pool::AbstractWorkerPool, args...; kwargs...) -> result

WorkerPool variant of remotecall_fetch(f, pid, ....). Waits for and takes a free worker from pool and performs a remotecall_fetch on it.

Examples

$ julia -p 3
julia> wp = WorkerPool([2, 3]);
julia> A = rand(3000);
julia> remotecall_fetch(maximum, wp, A)
0.9995177101692958

Distributed.remote_do — Method

remote_do(f, pool::AbstractWorkerPool, args...; kwargs...) -> nothing

WorkerPool variant of remote_do(f, pid, ....). Wait for and take a free worker from pool and perform a remote_do on it.

Distributed.@spawnat — Macro

@spawnat p expr

Create a closure around an expression and run the closure asynchronously on process p. Return a Future to the result. If p is the quoted literal symbol :any, then the system will pick a processor to use automatically.

Examples

julia> addprocs(3);
julia> f = @spawnat 2 myid()
Future(2, 1, 3, nothing)
julia> fetch(f)
2
julia> f = @spawnat :any myid()
Future(3, 1, 7, nothing)
julia> fetch(f)
3

Julia 1.3

The :any argument is available as of Julia 1.3.

Distributed.@fetch — Macro

@fetch expr

Equivalent to fetch(@spawnat :any expr). See fetch and @spawnat.

Examples

julia> addprocs(3);
julia> @fetch myid()
2
julia> @fetch myid()
3
julia> @fetch myid()
4
julia> @fetch myid()
2

Distributed.@fetchfrom — Macro

@fetchfrom

Equivalent to fetch(@spawnat p expr). See fetch and @spawnat.

Examples

julia> addprocs(3);
julia> @fetchfrom 2 myid()
2
julia> @fetchfrom 4 myid()
4

Distributed.@distributed — Macro

@distributed

A distributed memory, parallel for loop of the form :

@distributed [reducer] for var = range
    body
end

The specified range is partitioned and locally executed across all workers. In case an optional reducer function is specified, @distributed performs local reductions on each worker with a final reduction on the calling process.

Note that without a reducer function, @distributed executes asynchronously, i.e. it spawns independent tasks on all available workers and returns immediately without waiting for completion. To wait for completion, prefix the call with @sync, like :

@sync @distributed for var = range
    body
end

Distributed.@everywhere — Macro

@everywhere [procs()] expr

Execute an expression under Main on all procs. Errors on any of the processes are collected into a CompositeException and thrown. For example:

@everywhere bar = 1

will define Main.bar on all current processes. Any processes added later (say with addprocs()) will not have the expression defined.

Unlike @spawnat, @everywhere does not capture any local variables. Instead, local variables can be broadcast using interpolation:

foo = 1
@everywhere bar = $foo

The optional argument procs allows specifying a subset of all processes to have execute the expression.

Similar to calling remotecall_eval(Main, procs, expr), but with two extra features:

- `using` and `import` statements run on the calling process first, to ensure
  packages are precompiled.
- The current source file path used by `include` is propagated to other processes.

Distributed.clear! — Method

clear!(syms, pids=workers(); mod=Main)

Clears global bindings in modules by initializing them to nothing. syms should be of type Symbol or a collection of Symbols . pids and mod identify the processes and the module in which global variables are to be reinitialized. Only those names found to be defined under mod are cleared.

An exception is raised if a global constant is requested to be cleared.

Distributed.remoteref_id — Function

remoteref_id(r::AbstractRemoteRef) -> RRID

Futures and RemoteChannels are identified by fields:

where - refers to the node where the underlying object/storage referred to by the reference actually exists.
whence - refers to the node the remote reference was created from. Note that this is different from the node where the underlying object referred to actually exists. For example calling RemoteChannel(2) from the master process would result in a where value of 2 and a whence value of 1.
id is unique across all references created from the worker specified by whence.

Taken together, whence and id uniquely identify a reference across all workers.

remoteref_id is a low-level API which returns a RRID object that wraps whence and id values of a remote reference.

Distributed.channel_from_id — Function

channel_from_id(id) -> c

A low-level API which returns the backing AbstractChannel for an id returned by remoteref_id. The call is valid only on the node where the backing channel exists.

Distributed.worker_id_from_socket — Function

worker_id_from_socket(s) -> pid

A low-level API which, given a IO connection or a Worker, returns the pid of the worker it is connected to. This is useful when writing custom serialize methods for a type, which optimizes the data written out depending on the receiving process id.

Distributed.cluster_cookie — Method

cluster_cookie() -> cookie

Return the cluster cookie.

Distributed.cluster_cookie — Method

cluster_cookie(cookie) -> cookie

Set the passed cookie as the cluster cookie, then returns it.

Cluster Manager Interface

This interface provides a mechanism to launch and manage Julia workers on different cluster environments. There are two types of managers present in Base: LocalManager, for launching additional workers on the same host, and SSHManager, for launching on remote hosts via ssh. TCP/IP sockets are used to connect and transport messages between processes. It is possible for Cluster Managers to provide a different transport.

Distributed.ClusterManager — Type

ClusterManager

Supertype for cluster managers, which control workers processes as a cluster. Cluster managers implement how workers can be added, removed and communicated with. SSHManager and LocalManager are subtypes of this.

Distributed.WorkerConfig — Type

WorkerConfig

Type used by ClusterManagers to control workers added to their clusters. Some fields are used by all cluster managers to access a host:

io – the connection used to access the worker (a subtype of IO or Nothing)
host – the host address (either a String or Nothing)
port – the port on the host used to connect to the worker (either an Int or Nothing)

Some are used by the cluster manager to add workers to an already-initialized host:

count – the number of workers to be launched on the host
exename – the path to the Julia executable on the host, defaults to "$(Sys.BINDIR)/julia" or "$(Sys.BINDIR)/julia-debug"
exeflags – flags to use when lauching Julia remotely

The userdata field is used to store information for each worker by external managers.

Some fields are used by SSHManager and similar managers:

tunnel – true (use tunneling), false (do not use tunneling), or nothing (use default for the manager)
multiplex – true (use SSH multiplexing for tunneling) or false
forward – the forwarding option used for -L option of ssh
bind_addr – the address on the remote host to bind to
sshflags – flags to use in establishing the SSH connection
max_parallel – the maximum number of workers to connect to in parallel on the host

Some fields are used by both LocalManagers and SSHManagers:

connect_at – determines whether this is a worker-to-worker or driver-to-worker setup call
process – the process which will be connected (usually the manager will assign this during addprocs)
ospid – the process ID according to the host OS, used to interrupt worker processes
environ – private dictionary used to store temporary information by Local/SSH managers
ident – worker as identified by the ClusterManager
connect_idents – list of worker ids the worker must connect to if using a custom topology
enable_threaded_blas – true, false, or nothing, whether to use threaded BLAS or not on the workers

Distributed.launch — Function

launch(manager::ClusterManager, params::Dict, launched::Array, launch_ntfy::Condition)

Implemented by cluster managers. For every Julia worker launched by this function, it should append a WorkerConfig entry to launched and notify launch_ntfy. The function MUST exit once all workers, requested by manager have been launched. params is a dictionary of all keyword arguments addprocs was called with.

Distributed.manage — Function

manage(manager::ClusterManager, id::Integer, config::WorkerConfig. op::Symbol)

Implemented by cluster managers. It is called on the master process, during a worker’s lifetime, with appropriate op values:

with :register/:deregister when a worker is added / removed from the Julia worker pool.
with :interrupt when interrupt(workers) is called. The ClusterManager should signal the appropriate worker with an interrupt signal.
with :finalize for cleanup purposes.

Base.kill — Method

kill(manager::ClusterManager, pid::Int, config::WorkerConfig)

Implemented by cluster managers. It is called on the master process, by rmprocs. It should cause the remote worker specified by pid to exit. kill(manager::ClusterManager.....) executes a remote exit() on pid.

Sockets.connect — Method

connect(manager::ClusterManager, pid::Int, config::WorkerConfig) -> (instrm::IO, outstrm::IO)

Implemented by cluster managers using custom transports. It should establish a logical connection to worker with id pid, specified by config and return a pair of IO objects. Messages from pid to current process will be read off instrm, while messages to be sent to pid will be written to outstrm. The custom transport implementation must ensure that messages are delivered and received completely and in order. connect(manager::ClusterManager.....) sets up TCP/IP socket connections in-between workers.

Distributed.init_worker — Function

init_worker(cookie::AbstractString, manager::ClusterManager=DefaultClusterManager())

Called by cluster managers implementing custom transports. It initializes a newly launched process as a worker. Command line argument --worker[=<cookie>] has the effect of initializing a process as a worker using TCP/IP sockets for transport. cookie is a cluster_cookie.

Distributed.start_worker — Function

start_worker([out::IO=stdout], cookie::AbstractString=readline(stdin); close_stdin::Bool=true, stderr_to_stdout::Bool=true)

start_worker is an internal function which is the default entry point for worker processes connecting via TCP/IP. It sets up the process as a Julia cluster worker.

host:port information is written to stream out (defaults to stdout).

The function reads the cookie from stdin if required, and listens on a free port (or if specified, the port in the --bind-to command line option) and schedules tasks to process incoming TCP connections and requests. It also (optionally) closes stdin and redirects stderr to stdout.

It does not return.

Distributed.process_messages — Function

process_messages(r_stream::IO, w_stream::IO, incoming::Bool=true)

Called by cluster managers using custom transports. It should be called when the custom transport implementation receives the first message from a remote worker. The custom transport must manage a logical connection to the remote worker and provide two IO objects, one for incoming messages and the other for messages addressed to the remote worker. If incoming is true, the remote peer initiated the connection. Whichever of the pair initiates the connection sends the cluster cookie and its Julia version number to perform the authentication handshake.