| Taskflow: A General-purpose Task-parallel Programming System

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Partitioning Algorithm

A partitioning algorithm allows applications to optimize parallel algorithms using different scheduling methods, such as static partitioning, dynamic partitioning, and guided partitioning.

# Define a Partitioner for Parallel Algorithms

A partitioner defines how to partition and distribute iterations to different workers when running parallel algorithms in Taskflow, such as tf::Taskflow::for_each and tf::Taskflow::transform. The following example shows how to create parallel-iteration tasks with different execution policies:

std::vector<int> data = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10}
// create different partitioners
tf::GuidedPartitioner guided_partitioner;
tf::StaticPartitioner static_partitioner;
tf::RandomPartitioner random_partitioner;
tf::DynamicPartitioner dynamic_partitioner;
// create four parallel-iteration tasks from the four execution policies
taskflow.for_each(data.begin(), data.end(), [](int i){}, guided_partitioner);
taskflow.for_each(data.begin(), data.end(), [](int i){}, static_partitioner);
taskflow.for_each(data.begin(), data.end(), [](int i){}, random_partitioner);
taskflow.for_each(data.begin(), data.end(), [](int i){}, dynamic_partitioner);
tf::DynamicPartitioner
class to create a dynamic partitioner for scheduling parallel algorithms
Definition partitioner.hpp:597
tf::GuidedPartitioner
class to create a guided partitioner for scheduling parallel algorithms
Definition partitioner.hpp:417
tf::RandomPartitioner
class to construct a random partitioner for scheduling parallel algorithms
Definition partitioner.hpp:737
tf::StaticPartitioner
class to construct a static partitioner for scheduling parallel algorithms
Definition partitioner.hpp:262

Each partitioner has a specific algorithm to partition iterations into a set of chunks and distribute chunks to workers. A chunk is the basic unit of work that will be run by a worker during the execution of parallel iterations. The following figure illustrates the scheduling diagram for three major partitioners, tf::StaticPartitioner, tf::DynamicPartitioner, and tf::GuidedPartitioner:

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 may 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.

Note
  By default, all parallel algorithms in Taskflow use tf::DefaultPartitioner, which is based on guided scheduling via tf::GuidedPartitioner.

# Define a Static Partitioner

Static partitioner splits iterations into `iter_size/chunk_size` chunks and distribute chunks to workers in order. If no chunk size is given (`chunk_size` is 0), Taskflow will partition iterations into chunks that are approximately equal in size. The following code creates a static partitioner with chunk size equal to 100:

tf::StaticPartitioner static_partitioner(100);

# Define a Dynamic Partitioner

Dynamic partitioner splits iterations into `iter_size/chunk_size` chunks and distribute chunks to workers without any specific order. If no chunk size is given (`chunk_size` is 0), Taskflow will use 1 for the minimum size of a partition. The following code creates a dynamic partitioner with chunk size equal to 2:

tf::DynamicPartitioner dynamic_partitioner(2);

# Define a Guided Partitioner

Guided partitioner dynamically decides the chunk size. The size of a chunk is proportional to the number of unassigned iterations divided by the number of the threads, and the size will gradually decrease to the specified chunk size (default 1). The last chunk may be smaller than the specified chunk size. If no chunk size is given (`chunk_size` is 0), Taskflow will use 1 for the minimum size of a partition. The following code creates a guided partitioner with chunk size equal to 10:

tf::GuidedPartitioner guided_partitioner(10);

In most situations, guided partitioner can achieve decent performance due to adaptive parallelism, especially for those with irregular and unbalanced workload per iteration. As a result, guided partitioner is used as the default partitioner for our parallel algorithms.

# Define a Closure Wrapper for a Partitioner

In addition to partition size, applications 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
tf::Taskflow
class to create a taskflow object
Definition taskflow.hpp:64

Each partitioner uses a default closure wrapper (tf::DefaultClosureWrapper) that does nothing but simply invokes the given closure to perform the ordinary partitioned task.

struct DefaultClosureWrapper {
template <typename C>
void operator()(C&& closure) const { std::forward<C>(closure)(); }
};