DynamicIters¶
Usage
use DynamicIters;
This module contains several iterators that can be used to drive a forall loop by performing dynamic and adaptive splitting of a range's iterations.
For more information, see UserDefined Parallel Zippered Iterators in Chapel. Bradford L. Chamberlain, SungEun Choi, Steven J. Deitz, Angeles Navarro. PGAS 2011: Fifth Conference on Partitioned Global Address Space Programming Models, October 2011.

config param
debugDynamicIters
: bool = false¶ Toggle debugging output.

iter
dynamic
(c: range(?), chunkSize: int, numTasks: int = 0)¶ Arguments:  c : range(?)  The range to iterate over. The length of the range must be greater than zero.
 chunkSize : int  The size of chunks to be yielded to each thread. Must be greater than zero.
 numTasks : int  The number of tasks to use. Must be >= zero. If this argument
has the value 0, it will use the value indicated by
dataParTasksPerLocale
.
Yields: Indices in the range
c
.This iterator is equivalent to the dynamic scheduling approach of OpenMP.
Given an input range
c
, each task is assigned chunks of sizechunkSize
fromc
(or the remaining iterations if there are fewer thanchunkSize
). This continues until there are no remaining iterations inc
.This iterator can be called in serial and zippered contexts.

iter
dynamic
(c: domain, chunkSize: int, numTasks: int = 0, parDim: int = 1) Arguments:  c : domain  The domain to iterate over. The rank of the domain must be greater than zero.
 chunkSize : int  The size of chunks to be yielded to each thread. Must be greater than zero
 numTasks : int  The number of tasks to use. Must be >= zero. If this argument
has the value 0, it will use the value indicated by
dataParTasksPerLocale
.  parDim : int  The index of the dimension to parallelize across. Must be > 0.
Must be <= the rank of the domain
c
. Defaults to 1.
Yields: Indices of the domain
c
Given an input domain
c
, each task is assigned slices ofc
. The chunks each havechunkSize
slices in them (or the remaining iterations if there are fewer thanchunkSize
). This continues until there are no remaining iterations in the dimension ofc
indicated byparDim
.This iterator can be called in serial and zippered contexts.

iter
guided
(c: range(?), numTasks: int = 0)¶ Arguments:  c : range(?)  The range to iterate over. Must have a length greater than zero.
 numTasks : int  The number of tasks to use. Must be >= zero. If this argument
has the value 0, it will use the value indicated by
dataParTasksPerLocale
.
Yields: Indices in the range
c
.This iterator is equivalent to the guided policy of OpenMP: Given an input range
c
, each task is assigned chunks of variable size, until there are no remaining iterations inc
. The size of each chunk is the number of unassigned iterations divided by the number of tasks,numTasks
. The size decreases approximately exponentially to 1. The splitting strategy is therefore adaptive.This iterator can be called in serial and zippered contexts.

iter
adaptive
(c: range(?), numTasks: int = 0)¶ Arguments:  c : range(?)  The range to iterate over. Must have a length greater than zero.
 numTasks : int  The number of tasks to use. Must be >= zero. If this argument
has the value 0, it will use the value indicated by
dataParTasksPerLocale
.
Yields: Indices in the range
c
.This iterator implements a naive adaptive binary splitting workstealing strategy: Initially the leader iterator distributes the range to split,
c
, evenly among thenumTasks
tasks.Then, each task performs adaptive splitting on its local subrange's iterations. When a task exhausts its local iterations, it steals and splits from the range of another task (the victim). The splitting method on the local range and on the victim range is binary: i.e. the size of each chunk is computed as the number of unassigned iterations divided by 2. There are three stealing strategies that can be selected at compile time using the config param
methodStealing
.This iterator can be called in serial and zippered contexts.

enum
Method
{ Whole = 0, RoundRobin = 1, WholeTail = 2 }¶ The enum used to represent adaptive methods.
Whole
Each task without work tries to steal from its neighbor range until it exhausts that range. Then the task continues with the next neighbor range, and so on until there is no more work. This is the default policy.RoundRobin
Each task without work tries to steal once from its neighbor range, next from the following neighbor range and so on in a roundrobin way until there is no more work.WholeTail
Similar to theWhole
method, but now the splitting in the victim range is performed from its tail.