Parmap (3)

Module Parmap

Module Parmap : sig end

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.

=== Setting and getting the default value for ncores ===

val set_default_ncores : int -> unit

val get_default_ncores : unit -> int

=== 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. ===

=== Parallel mapfold ===

val parmapfold : ?init:(int -> unit) -> ?finalize:(unit -> unit) -> ?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

=== Parallel fold ===

val parfold : ?init:(int -> unit) -> ?finalize:(unit -> unit) -> ?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 .

=== Parallel map ===

val parmap : ?init:(int -> unit) -> ?finalize:(unit -> unit) -> ?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.

=== Parallel iteration ===

val pariter : ?init:(int -> unit) -> ?finalize:(unit -> unit) -> ?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.

=== Parallel mapfold, indexed ===

val parmapifold : ?init:(int -> unit) -> ?finalize:(unit -> unit) -> ?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

=== Parallel map, indexed ===

val parmapi : ?init:(int -> unit) -> ?finalize:(unit -> unit) -> ?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

=== Parallel iteration, indexed ===

val pariteri : ?init:(int -> unit) -> ?finalize:(unit -> unit) -> ?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

=== Parallel map on arrays ===

val array_parmap : ?init:(int -> unit) -> ?finalize:(unit -> unit) -> ?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.

=== Parallel map on arrays, indexed ===

val array_parmapi : ?init:(int -> unit) -> ?finalize:(unit -> unit) -> ?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

=== 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 : ?init:(int -> unit) -> ?finalize:(unit -> unit) -> ?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.

=== Parallel map on float arrays, indexed ===

val array_float_parmapi : ?init:(int -> unit) -> ?finalize:(unit -> unit) -> ?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 ===

=== Debugging ===

val debugging : bool -> unit

=== Enable or disable debugging code in the library; default: false ===

=== Helper function for redirection of stdout and stderr ===

val redirect : ?path:string -> id:int -> unit

=== Helper function that redirects stdout and stderr to files located in the directory path, carrying names of the shape stdout.NNN and stderr.NNN where NNN is the id of the used core. Useful when writing initialisation functions to be passed as init argument to the parallel combinators. The default value for path is /tmp/.parmap.PPPP with PPPP the process id of the main program. ===