docs/classtf_1_1PartitionerBase.html
| | Taskflow: A General-purpose Task-parallel Programming System |
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Public Types | Public Member Functions | Static Public Attributes | List of all members
tf::PartitionerBase< C > Class Template Reference
class to derive a partitioner for scheduling parallel algorithms More...
#include <taskflow/algorithm/partitioner.hpp>
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| using | closure_wrapper_type = C |
| | the closure type
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| | PartitionerBase ()=default |
| | default constructor
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| | PartitionerBase (size_t chunk_size) |
| | construct a partitioner with the given chunk size
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| | PartitionerBase (size_t chunk_size, C &&closure_wrapper) |
| | construct a partitioner with the given chunk size and closure wrapper
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| size_t | chunk_size () const |
| | query the chunk size of this partitioner
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| void | chunk_size (size_t cz) |
| | update the chunk size of this partitioner
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| const C & | closure_wrapper () const |
| | acquire an immutable access to the closure wrapper object
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| C & | closure_wrapper () |
| | acquire a mutable access to the closure wrapper object
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| template<typename F> |
| void | closure_wrapper (F &&fn) |
| | modify the closure wrapper object
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| template<typename F> |
| TF_FORCE_INLINE decltype(auto) | operator() (F &&callable) |
| | wraps the given callable with the associated closure wrapper
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| 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)
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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:
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.
AttentionGiving 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();
tf::FlowBuilder::for_each_index
Task for_each_index(B first, E last, S step, C callable, P part=P())
constructs an index-based parallel-for task
class to construct a static partitioner for scheduling parallel algorithms
Definition partitioner.hpp:262
class to create a taskflow object
Definition taskflow.hpp:64
AttentionThe 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
Maintained by Dr. Tsung-Wei Huang — Generated by 1.13.1