A type-safe parallel library built on top of Async_rpc.
module Worker = Rpc_parallel.Make (T : Worker_spec)
The Worker
module can be used to spawn new workers, either locally or remotely, and
run functions on these workers. T
specifies which functions can be run on a
Worker.t
as well as the implementations for these functions. In addition, T
specifies worker states and connection states. See README for more details
module Remote_executable : sig ... end
module Executable_location : sig ... end
module Managed = Parallel_managed
module Map_reduce : sig ... end
A parallel map/reduce library. See examples/add_numbers.ml and examples/number_stats.ml for examples.
include Parallel
include Rpc_parallel__.Parallel_intf.Parallel
module type Worker : Rpc_parallel__.Parallel_intf.Worker with type (w, q, r) _function := (w, q, r) Function.t
module type Functions = Rpc_parallel__.Parallel_intf.Functions
module type Creator : Rpc_parallel__.Parallel_intf.Creator with type (w, q, r) _function := (w, q, r) Function.t and type (w, q, r) _direct := (w, q, r) Function.Direct_pipe.t
module type Worker_spec : Rpc_parallel__.Parallel_intf.Worker_spec with type (w, q, r) _function := (w, q, r) Function.t and type (w, q, r) _direct := (w, q, r) Function.Direct_pipe.t
module Make : functor (S : Worker_spec) -> Worker with type a functions := a S.functions and type worker_state_init_arg := S.Worker_state.init_arg and type connection_state_init_arg := S.Connection_state.init_arg
module Worker = Make(T)
val start_app : ?rpc_max_message_size:int ‑> ?rpc_handshake_timeout:Core.Time.Span.t ‑> ?rpc_heartbeat_config:Async.Rpc.Connection.Heartbeat_config.t ‑> Async.Command.t ‑> unit
start_app command
should be called from the top-level in order to start the parallel
application. This function will parse certain environment variables and determine
whether to start as a master or a worker.
rpc_max_message_size
, rpc_handshake_timeout
, rpc_heartbeat_config
can be used
to alter the rpc defaults. These rpc settings will be used for all connections.
This can be useful if you have long async jobs.
module State : sig ... end
Use State.get
to query whether the current process has been initialized as an rpc
parallel master (start_app
or init_master_exn
has been called). We return a
State.t
rather than a bool
so that you can require evidence at the type level.
If you want to certify, as a precondition, for some function that start_app
was
used, require a State.t
as an argument. If you don't need the State.t
anymore,
just pattern match on it.
module Expert : sig ... end
If you want more direct control over your executable, you can use the Expert
module instead of start_app
. If you use Expert
, you are responsible for starting
the master and worker rpc servers. worker_command_args
will be the arguments sent
to each spawned worker. Running your executable with these args must follow a code
path that calls worker_init_before_async_exn
and then start_worker_server_exn
.
An easy way to do this is to use worker_command
.
module Parallel : sig ... end
module Parallel_managed : sig ... end
This module is primarily meant for backwards compatibility with code that used earlier
versions of Rpc_parallel
. Please consider using the Parallel.Make()
functor
instead as the semantics are more transparent and intuitive.