swh:1:snp:2ad4e6cab37cbb98f3063cd1928c837156c95438
Tip revision: d56ae79e43ff1b37534370911c8a78ec7f38d437 authored by Roberto Di Cosmo on 24 May 2012, 20:01:01 UTC
Bumped up version number to 0.9.9
Bumped up version number to 0.9.9
Tip revision: d56ae79
parmap.mli
(**************************************************************************)
(* ParMap: a simple library to perform Map computations on a multi-core *)
(* *)
(* Author(s): Marco Danelutto, Roberto Di Cosmo *)
(* *)
(* This library is free software: you can redistribute it and/or modify *)
(* it under the terms of the GNU Lesser General Public License as *)
(* published by the Free Software Foundation, either version 2 of the *)
(* License, or (at your option) any later version. A special linking *)
(* exception to the GNU Lesser General Public License applies to this *)
(* library, see the LICENSE file for more information. *)
(**************************************************************************)
(** Module [Parmap]: efficient parallel map, fold and mapfold on lists and
arrays on multicores.
All the primitives allow to control the granularity of the parallelism
via an optional parameter [chunksize]: if [chunksize] is omitted, the
input sequence is split evenly among the available cores; if [chunksize]
is specified, the input data is split in chunks of size [chunksize] and
dispatched to the available cores using an on demand strategy that
ensures automatic load balancing.
A specific primitive [array_float_parmap] is provided for fast operations on float arrays.
*)
(** {6 Sequence type, subsuming lists and arrays} *)
type 'a sequence = L of 'a list | A of 'a array;;
(** The [parmapfold], [parfold] and [parmap] generic functions, for efficiency reasons,
convert the input data into an array internally, so we provide the ['a sequence] type
to allow passing an array directly as input.
If you want to perform a parallel map operation on an array, use [array_parmap] or [array_float_parmap] instead.
*)
(** {6 Parallel mapfold} *)
val parmapfold : ?ncores:int -> ?chunksize:int -> ('a -> 'b) -> 'a sequence -> ('b-> 'c -> 'c) -> 'c -> ('c->'c->'c) -> 'c
(** [parmapfold ~ncores:n f (L l) op b concat ] computes [List.fold_right op (List.map f l) b]
by forking [n] processes on a multicore machine.
You need to provide the extra [concat] operator to combine the partial results of the
fold computed on each core. If 'b = 'c, then [concat] may be simply [op].
The order of computation in parallel changes w.r.t. sequential execution, so this
function is only correct if [op] and [concat] are associative and commutative.
If the optional [chunksize] parameter is specified,
the processes compute the result in an on-demand fashion
on blocks of size [chunksize].
[parmapfold ~ncores:n f (A a) op b concat ] computes [Array.fold_right op (Array.map f a) b]
*)
(** {6 Parallel fold} *)
val parfold: ?ncores:int -> ?chunksize:int -> ('a -> 'b -> 'b) -> 'a sequence -> 'b -> ('b->'b->'b) -> 'b
(** [parfold ~ncores:n op (L l) b concat] computes [List.fold_right op l b]
by forking [n] processes on a multicore machine.
You need to provide the extra [concat] operator to combine the partial results of the
fold computed on each core. If 'b = 'c, then [concat] may be simply [op].
The order of computation in parallel changes w.r.t. sequential execution, so this
function is only correct if [op] and [concat] are associative and commutative.
If the optional [chunksize] parameter is specified,
the processes compute the result in an on-demand fashion
on blocks of size [chunksize].
[parfold ~ncores:n op (A a) b concat] similarly computes [Array.fold_right op a b].
*)
(** {6 Parallel map} *)
val parmap : ?ncores:int -> ?chunksize:int -> ('a -> 'b) -> 'a sequence -> 'b list
(** [parmap ~ncores:n f (L l) ] computes [List.map f l]
by forking [n] processes on a multicore machine.
[parmap ~ncores:n f (A a) ] computes [Array.map f a]
by forking [n] processes on a multicore machine.
If the optional [chunksize] parameter is specified,
the processes compute the result in an on-demand fashion
on blocks of size [chunksize]; this provides automatic
load balancing for unbalanced computations, but the order
of the result is no longer guaranteed to be preserved. *)
(** {6 Parallel iteration} *)
val pariter : ?ncores:int -> ?chunksize:int -> ('a -> unit) -> 'a sequence -> unit
(** [pariter ~ncores:n f (L l) ] computes [List.iter f l]
by forking [n] processes on a multicore machine.
[parmap ~ncores:n f (A a) ] computes [Array.iter f a]
by forking [n] processes on a multicore machine.
If the optional [chunksize] parameter is specified,
the processes perform the computation in an on-demand fashion
on blocks of size [chunksize]; this provides automatic
load balancing for unbalanced computations. *)
(** {6 Parallel mapfold, indexed} *)
val parmapifold : ?ncores:int -> ?chunksize:int -> (int -> 'a -> 'b) -> 'a sequence -> ('b-> 'c -> 'c) -> 'c -> ('c->'c->'c) -> 'c
(** Like parmapfold, but the map function gets as an extra argument
the index of the mapped element *)
(** {6 Parallel map} *)
val parmapi : ?ncores:int -> ?chunksize:int -> (int -> 'a -> 'b) -> 'a sequence -> 'b list
(** Like parmap, but the map function gets as an extra argument
the index of the mapped element *)
(** {6 Parallel iteration, indexed} *)
val pariteri : ?ncores:int -> ?chunksize:int -> (int -> 'a -> unit) -> 'a sequence -> unit
(** Like pariter, but the iterated function gets as an extra argument
the index of the sequence element *)
(** {6 Parallel map on arrays} *)
val array_parmap : ?ncores:int -> ?chunksize:int -> ('a -> 'b) -> 'a array -> 'b array
(** [array_parmap ~ncores:n f a ] computes [Array.map f a]
by forking [n] processes on a multicore machine.
If the optional [chunksize] parameter is specified,
the processes compute the result in an on-demand fashion
on blochs of size [chunksize]; this provides automatic
load balancing for unbalanced computations, but the order
of the result is no longer guaranteed to be preserved. *)
val array_parmapi : ?ncores:int -> ?chunksize:int -> (int -> 'a -> 'b) -> 'a array -> 'b array
(** Like array_parmap, but the map function gets as an extra argument
the index of the mapped element *)
(** {6 Parallel map on float arrays } *)
exception WrongArraySize
type buf
val init_shared_buffer : float array -> buf
(** [init_shared_buffer a] creates a new memory mapped shared buffer big enough to hold a float array of the size of [a].
This buffer can be reused in a series of calls to [array_float_parmap], avoiding the cost of reallocating it each time. *)
val array_float_parmap : ?ncores:int -> ?chunksize:int -> ?result: float array -> ?sharedbuffer: buf -> ('a -> float) -> 'a array -> float array
(** [array_float_parmap ~ncores:n f a ] computes [Array.map f a] by forking
[n] processes on a multicore machine, and preallocating the resulting
array as shared memory, which allows significantly more efficient
computation than calling the generic array_parmap function. If the
optional [chunksize] parameter is specified, the processes compute the
result in an on-demand fashion on blochs of size [chunksize]; this
provides automatic load balancing for unbalanced computations, *and* the
order of the result is still guaranteed to be preserved.
In case you already have at hand an array where to store the result, you
can squeeze out some more cpu cycles by passing it as optional parameter
[result]: this will avoid the creation of a result array, which can be
costly for very large data sets. Raises [WrongArraySize] if [result] is too
small to hold the data.
It is possible to share the same preallocated shared memory space across
calls, by initialising the space calling [init_shared_buffer a] and
passing the result as the optional [sharedbuffer] parameter to each
subsequent call to [array_float_parmap]. Raises WrongArraySize if
[sharedbuffer] is too small to hold the input data. *)
val array_float_parmapi : ?ncores:int -> ?chunksize:int -> ?result: float array -> ?sharedbuffer: buf -> (int -> 'a -> float) -> 'a array -> float array
(** Like array_float_parmap, but the map function gets as an extra argument
the index of the mapped element *)