class to derive a partitioner for scheduling parallel algorithms More...

#include <taskflow/algorithm/partitioner.hpp>

Public Types
using	closure_wrapper_type = C
	the closure type

Public Member Functions
	PartitionerBase ()=default
	default constructor

	PartitionerBase (size_t chunk_size)
	construct a partitioner with the given chunk size

	PartitionerBase (size_t chunk_size, C &&closure_wrapper)
	construct a partitioner with the given chunk size and closure wrapper

size_t	chunk_size () const
	query the chunk size of this partitioner

void	chunk_size (size_t cz)
	update the chunk size of this partitioner

const C &	closure_wrapper () const
	acquire an immutable access to the closure wrapper object

C &	closure_wrapper ()
	acquire a mutable access to the closure wrapper object

template<typename F>
void	closure_wrapper (F &&fn)
	modify the closure wrapper object

template<typename F>
TF_FORCE_INLINE decltype(auto)	operator() (F &&callable)
	wraps the given callable with the associated closure wrapper

Static Public Attributes
static constexpr bool	is_default_wrapper_v = std::is_same_v<C, DefaultClosureWrapper>
	indicating if the given closure wrapper is a default wrapper (i.e., empty)

Detailed Description

template<typename C = DefaultClosureWrapper>
class tf::PartitionerBase< C >

class to derive a partitioner for scheduling parallel algorithms

Template Parameters

C	closure wrapper type

The class provides base methods to derive a partitioner that can be used to schedule parallel iterations (e.g., tf::Taskflow::for_each).

An partitioner defines the scheduling method for running parallel algorithms, such tf::Taskflow::for_each, tf::Taskflow::reduce, and so on. By default, we provide the following partitioners:

tf::GuidedPartitioner to enable guided scheduling algorithm of adaptive chunk size
tf::DynamicPartitioner to enable dynamic scheduling algorithm of equal chunk size
tf::StaticPartitioner to enable static scheduling algorithm of static chunk size
tf::RandomPartitioner to enable random scheduling algorithm of random chunk size

Depending on applications, partitioning algorithms can impact the performance a lot. For example, if a parallel-iteration workload contains a regular work unit per iteration, tf::StaticPartitioner can deliver the best performance. On the other hand, if the work unit per iteration is irregular and unbalanced, tf::GuidedPartitioner or tf::DynamicPartitioner can outperform tf::StaticPartitioner. In most situations, tf::GuidedPartitioner can deliver decent performance and is thus used as our default partitioner.

Attention: Giving the partition size of 0 lets the Taskflow runtime automatically determines the partition size for the given partitioner.

In addition to partition size, the application can specify a closure wrapper for a partitioner. A closure wrapper allows the application to wrap a partitioned task (i.e., closure) with a custom function object that performs additional tasks. For example:

std::atomic<int> count = 0;
tf::Taskflow taskflow;
taskflow.for_each_index(0, 100, 1, 
  [](){                 
    printf("%d\n", i); 
  },
  tf::StaticPartitioner(0, [](auto&& closure){
    // do something before invoking the partitioned task
    // ...
    
    // invoke the partitioned task
    closure();
 
    // do something else after invoking the partitioned task
    // ...
  }
);
executor.run(taskflow).wait();

Attention: The default closure wrapper (tf::DefaultClosureWrapper) does nothing but invoke the partitioned task (closure).

The documentation for this class was generated from the following file:

taskflow/algorithm/partitioner.hpp

Public Types

Public Member Functions

Static Public Attributes

Detailed Description