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mesytec-mnode/external/taskflow-3.8.0/doxygen/algorithms/pipeline.dox
Florian Lüke e426fb7628 add taskflow-3.8.0
2025-01-04 01:25:05 +01:00

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namespace tf {
/** @page TaskParallelPipeline Task-parallel Pipeline
@tableofcontents
%Taskflow provides a @em task-parallel pipeline programming framework for you to
implement a pipeline algorithm.
%Pipeline parallelism refers to a parallel execution of multiple data
tokens through a linear chain of pipes or stages.
Each stage processes the data token sent from the previous stage, applies the
given callable to that data token, and then sends the result to the next stage.
Multiple data tokens can be processed simultaneously across different stages.
@section TaskParallelPipelineIncludeHeaderFile Include the Header
You need to include the header file, <tt>%taskflow/algorithm/pipeline.hpp</tt>,
for implementing task-parallel pipeline algorithms.
@code{.cpp}
#include <taskflow/algorithm/pipeline.hpp>
@endcode
@section UnderstandPipelineScheduling Understand the Pipeline Scheduling Framework
A tf::Pipeline object is a @em composable graph to create a
<i>pipeline scheduling framework</i> through a module task in a taskflow
(see @ref ComposableTasking).
Unlike the conventional pipeline programming frameworks (e.g., Intel TBB Parallel %Pipeline),
%Taskflow's pipeline algorithm does not provide any data abstraction,
which often restricts users from optimizing data layouts in their applications,
but a flexible framework for users to customize their application data atop
an efficient pipeline scheduling framework.
@dotfile images/pipeline_our_structure.dot
The figure above gives an example of our pipeline scheduling framework.
The framework consists of three @em pipes (serial-parallel-serial stages) and
four @em lines (maximum parallelism),
where each line processes at most one data token.
A pipeline of three pipes and four lines will propagate each data token through a sequential chain of
three pipes and can simultaneously process up to four data tokens at the four lines.
Each edge represents a task dependency.
For example, the edge from @c pipe-0 to @c pipe-1 in line @c 0 represents the task dependency
between the first and the second pipes in the first line;
the edge from @c pipe-0 in line @c 0 to @c pipe-0 in line @c 1 represents the task dependency
between two adjacent lines when processing two data tokens at the same pipe.
Each pipe can be either a <i>serial</i> type or
a <i>parallel</i> type,
where a serial pipe processes data tokens sequentially and
a parallel pipe processes different data tokens simultaneously.
@note
Due to the nature of pipeline, %Taskflow requires the first pipe to be a serial type.
The pipeline scheduling algorithm operates in a circular fashion with a factor of line count.
@section CreateATaskParallelPipelineModuleTask Create a Task-parallel Pipeline Module Task
%Taskflow leverages modern C++ and template techniques to strike a balance
between the @em expressiveness and @em generality
in designing the pipeline programming model.
In general, there are three steps to create a task-parallel pipeline application:
1. Define the pipeline structure (e.g., pipe type, pipe callable, stopping rule, line count)
2. Define the data storage and layout, if needed for the application
3. Define the pipeline taskflow graph using composition
The following code creates a pipeline scheduling framework
using the example from the previous section.
The framework schedules a total of five <i>scheduling tokens</i> labeled from 0 to 4.
The first pipe stores the token identifier in a custom data storage, `buffer`,
and each of the rest pipes
adds one to the input data from the result of the previous pipe and stores the result into the corresponding
line entry in the buffer.
@code{.cpp}
1: tf::Taskflow taskflow;
2: tf::Executor executor;
3:
4: // maximum parallelism - each line processes one token at a time
5: const size_t num_lines = 4;
6:
7: // custom data storage
8: std::array<int, num_lines> buffer;
9:
10: // the pipeline consists of three pipes (serial-parallel-serial)
11: // and up to four concurrent scheduling tokens
12: tf::Pipeline pl(num_lines,
13: tf::Pipe{tf::PipeType::SERIAL, [&buffer](tf::Pipeflow& pf) {
14: // generate only 5 scheduling tokens
15: if(pf.token() == 5) {
16: pf.stop();
17: }
18: // save the result of this pipe into the buffer
19: else {
20: printf("pipe 0: input token = %zu\n", pf.token());
21: buffer[pf.line()] = pf.token();
22: }
23: }},
24:
25: tf::Pipe{tf::PipeType::PARALLEL, [&buffer](tf::Pipeflow& pf) {
26: printf(
27: "pipe 1: input buffer[%zu] = %d\n",
28: pf.line(), buffer[pf.line()]
29: );
30: // propagate the previous result to this pipe by adding one
31: buffer[pf.line()] = buffer[pf.line()] + 1;
32: }},
33:
34: tf::Pipe{tf::PipeType::SERIAL, [&buffer](tf::Pipeflow& pf) {
35: printf(
36: "pipe 2: input buffer[%zu] = %d\n",
37: pf.line(), buffer[pf.line()]
38: );
39: // propagate the previous result to this pipe by adding one
40: buffer[pf.line()] = buffer[pf.line()] + 1;
41: }}
42: );
43:
44: // build the pipeline graph using composition
45: tf::Task pipeline = taskflow.composed_of(pl).name("pipeline");
46:
47: // execute the taskflow
48: executor.run(taskflow).wait();
@endcode
Debrief:
@li Lines 4-5 define the structure of the pipeline scheduling framework
@li Line 8 defines the data storage as an one-dimensional array of @c num_lines integers
@li Line 12 defines the number of lines in the pipeline
@li Lines 13-23 define the first serial pipe, which will stop the pipeline scheduling at the fifth token
@li Lines 25-32 define the second parallel pipe
@li Lines 34-41 define the third serial pipe
@li Line 45 defines the pipeline taskflow graph using composition
@li Line 48 executes the taskflow
%Taskflow leverages @ref RuntimeTasking and @ref ComposableTasking to implement
the pipeline scheduling framework.
The taskflow graph of this pipeline example is shown as follows,
where 1) one condition task is used to decide which runtime task to run and
2) four runtime tasks are used to schedule tokens at four parallel lines, respectively.
@dotfile images/pipeline_basic_dependency_graph.dot
In this example, we customize the data storage, @c buffer, as an one-dimensional array
of 4 integers, since the pipeline structure defines only four parallel lines.
Each entry of @c buffer stores stores the data being processed in the corresponding line.
For example, <tt>buffer[1]</tt> stores the processed data at line @c 1.
The following figure shows the data layout of @c buffer.
@dotfile images/pipeline_memory_layout.dot
@note
In practice, you may need to add padding to the data type of the buffer or
align it with the cacheline size to avoid false sharing.
If the data type varies at different pipes, you can use @std_variant to store the
data types in a uniform storage.
For each scheduling token,
you can use tf::Pipeflow::line() to get its line identifier
and tf::Pipeflow::pipe() to get its pipe identifier.
For example, if a scheduling token is at the third pipe of the forth line,
tf::Pipeflow::line() will return @c 3 and tf::Pipeflow::pipe() will return @c 2
(index starts from 0).
To stop the execution of the pipeline, you need to call tf::Pipeflow::stop() at the first pipe.
Once the stop signal has been triggered, the pipeline will stop scheduling any new tokens
after the callable.
As we can see from this example, tf::Pipeline gives you the full control to customize
your application data on top of a pipeline scheduling framework.
@note
1. Calling tf::Pipeflow::stop() not at the first pipe has no effect on the pipeline scheduling.
2. In most cases, std::thread::hardware_concurrency is a good number for line count.
Our pipeline algorithm schedules tokens in a @em circular manner, with a factor of @c num_lines.
That is, token @c t will be processed at line <tt>t % num_lines</tt>.
The following snippet shows one of the possible outputs of this pipeline program:
@code{.bash}
pipe 0: input token = 0
pipe 1: input buffer[0] = 0
pipe 2: input buffer[0] = 1
pipe 0: input token = 1
pipe 1: input buffer[1] = 1
pipe 2: input buffer[1] = 2
pipe 0: input token = 2
pipe 1: input buffer[2] = 2
pipe 2: input buffer[2] = 3
pipe 0: input token = 3
pipe 1: input buffer[3] = 3
pipe 2: input buffer[3] = 4
pipe 0: input token = 4
pipe 1: input buffer[0] = 4
pipe 2: input buffer[0] = 5
@endcode
There are a total of five tokens running through three pipes.
Each pipes prints its input data value, except the first pipe that prints its token identifier.
Since the second pipe is a parallel pipe, the output can interleave.
@section ConnectWithTasks Connect Pipeline with Other Tasks
You can connect the pipeline module task with other tasks to create a taskflow application
that embeds one or multiple pipeline algorithms.
We describe three common examples below:
+ @ref IterateAPipeline
+ @ref ConcatenateTwoPipelines
+ @ref DefineMultipleTaskParallelPipelines
@subsection IterateAPipeline Example 1: Iterate a Pipeline
This example emulates a data streaming application that iteratively runs a stream of data
through a pipeline using conditional tasking.
The taskflow graph consists of one pipeline module task and one condition task.
The pipeline module task processes a stream of data.
The condition task decides the availability of data and reruns the pipeline
when the next stream of data becomes available.
@code{.cpp}
1: tf::Taskflow taskflow;
2: tf::Executor executor;
3:
4: const size_t num_lines = 4; // maximum parallelism of the pipeline
5:
6: int i = 0, N = 0;
7: // custom data storage
8: std::array<int, num_lines> buffer;
9:
10: // the pipeline consists of three pipes (serial-parallel-serial)
11: // and up to four concurrent scheduling tokens
12: tf::Pipeline pl(num_lines,
13: tf::Pipe{tf::PipeType::SERIAL, [&i, &buffer](tf::Pipeflow& pf) {
14: // only 5 scheduling tokens are processed
15: if(i++ == 5) {
16: pf.stop();
17: }
18: // save the result of this pipe into the buffer
19: else {
20: printf("stage 0: input token = %zu\n", pf.token());
21: buffer[pf.line()] = pf.token();
22: }
23: }},
24:
25: tf::Pipe{tf::PipeType::PARALLEL, [&buffer](tf::Pipeflow& pf) {
26: printf(
27: "stage 1: input buffer[%zu] = %d\n",
28: pf.line(), buffer[pf.line()]
29: );
30: // propagate the previous result to this pipe by adding one
31: buffer[pf.line()] = buffer[pf.line()] + 1;
32: }},
33:
34: tf::Pipe{tf::PipeType::SERIAL, [&buffer](tf::Pipeflow& pf) {
35: printf(
36: "stage 2: input buffer[%zu] = %d\n",
37: pf.line(), buffer[pf.line()]
38: );
39: // propagate the previous result to this pipe by adding one
40: buffer[pf.line()] = buffer[pf.line()] + 1;
41: }}
42: );
43:
44: tf::Task conditional = taskflow.emplace([&N, &i](){
45: i = 0;
46: if (++N < 2) {
47: std::cout << "Rerun the pipeline\n";
48: return 0;
49: }
50: else {
51: return 1;
52: }
53: }).name("conditional");
54:
55: // build the pipeline graph using composition
56: tf::Task pipeline = taskflow.composed_of(pl)
57: .name("pipeline");
58: tf::Task initial = taskflow.emplace([](){ std::cout << "initial\n"; })
59: .name("initial");
60: tf::Task stop = taskflow.emplace([](){ std::cout << "stop\n"; })
61: .name("stop");
62:
63: // specify the graph dependency
64: initial.precede(pipeline);
65: pipeline.precede(conditional);
66: conditional.precede(pipeline, stop);
67:
68: // execute the taskflow
69: executor.run(taskflow).wait();
@endcode
Debrief:
@li Lines 4-5 define the structure of the pipeline scheduling framework
@li Line 8 defines the data storage as an one-dimensional array (@c num_lines integers)
@li Line 12 defines the number of lines in the pipeline
@li Lines 13-23 define the first serial pipe, which will stop the pipeline scheduling when @c i is @c 5
@li Lines 25-32 define the second parallel pipe
@li Lines 34-41 define the third serial pipe
@li Lines 44-53 define a condition task which returns 0 when @c N is less than @c 2, otherwise returns @c 1
@li Line 45 resets variable @c i
@li Lines 56-57 define the pipeline graph using composition
@li Lines 58-61 define two static tasks
@li Line 64-66 define the task dependency
@li Line 69 executes the taskflow
The taskflow graph of this pipeline example is illustrated as follows:
@dotfile images/pipeline_example1.dot
The following snippet shows one of the possible outputs:
@code{.bash}
initial
stage 0: input token = 0
stage 1: input buffer[0] = 0
stage 2: input buffer[0] = 1
stage 0: input token = 1
stage 1: input buffer[1] = 1
stage 2: input buffer[1] = 2
stage 0: input token = 2
stage 1: input buffer[2] = 2
stage 2: input buffer[2] = 3
stage 0: input token = 3
stage 1: input buffer[3] = 3
stage 2: input buffer[3] = 4
stage 0: input token = 4
stage 1: input buffer[0] = 4
stage 2: input buffer[0] = 5
Rerun the pipeline
stage 0: input token = 5
stage 1: input buffer[1] = 5
stage 2: input buffer[1] = 6
stage 0: input token = 6
stage 1: input buffer[2] = 6
stage 2: input buffer[2] = 7
stage 0: input token = 7
stage 1: input buffer[3] = 7
stage 2: input buffer[3] = 8
stage 0: input token = 8
stage 1: input buffer[0] = 8
stage 2: input buffer[0] = 9
stage 0: input token = 9
stage 1: input buffer[1] = 9
stage 2: input buffer[1] = 10
stop
@endcode
The pipeline runs twice as controlled by the condition task @c conditional.
The starting token in the second run of the pipeline is @c 5 rather than @c 0
because the pipeline keeps a stateful number of tokens.
The last token is @c 9, which means the pipeline processes in total @c 10 scheduling tokens.
The first five tokens (token @c 0 to @c 4) are processed in the first run, and
the remaining five tokens (token @c 5 to @c 9) are processed in the second run.
In the condition task, we use @c N as a decision-making counter to process the next stream of data.
@subsection ConcatenateTwoPipelines Example 2: Concatenate Two Pipelines
This example demonstrates two concatenated pipelines where
a sequence of data tokens run synchronously from one pipeline to another pipeline.
The first pipeline task precedes the second pipeline task.
@code{.cpp}
1: tf::Taskflow taskflow("pipeline");
2: tf::Executor executor;
3:
4: // define the maximum parallelism of the pipeline
5: const size_t num_lines = 4;
6:
7: // custom data storage
8: std::array<int, num_lines> buffer_1;
9: std::array<int, num_lines> buffer_2;
10:
11: // the pipeline_1 consists of three pipes (serial-parallel-serial)
12: // and up to four concurrent scheduling tokens
13: tf::Pipeline pl_1(num_lines,
14: tf::Pipe{tf::PipeType::SERIAL, [&buffer_1](tf::Pipeflow& pf) mutable{
15: // generate only 4 scheduling tokens
16: if(pf.token() == 4) {
17: pf.stop();
18: }
19: // save the result of this pipe into the buffer
20: else {
21: printf("pipeline 1, pipe 0: input token = %zu\n", pf.token());
22: buffer_1[pf.line()] = pf.token();
23: }
24: }},
25:
26: tf::Pipe{tf::PipeType::PARALLEL, [&buffer_1](tf::Pipeflow& pf) {
27: printf(
28: "pipeline 1, pipe 1: input buffer_1[%zu] = %d\n",
29: pf.line(), buffer_1[pf.line()]
30: );
31: // propagate the previous result to this pipe by adding one
32: buffer_1[pf.line()] = buffer_1[pf.line()] + 1;
33: }},
34:
35: tf::Pipe{tf::PipeType::SERIAL, [&buffer_1](tf::Pipeflow& pf) {
36: printf(
37: "pipeline 1, pipe 2: input buffer_1[%zu] = %d\n",
38: pf.line(), buffer_1[pf.line()]
39: );
40: // propagate the previous result to this pipe by adding one
41: buffer_1[pf.line()] = buffer_1[pf.line()] + 1;
42: }}
43: );
44:
45: // the pipeline_2 consists of three pipes (serial-parallel-serial)
46: // and up to four concurrent scheduling tokens
47: tf::Pipeline pl_2(num_lines,
48: tf::Pipe{tf::PipeType::SERIAL,
49: [&buffer_2, &buffer_1](tf::Pipeflow& pf) mutable{
50: // generate only 4 scheduling tokens
51: if(pf.token() == 4) {
52: pf.stop();
53: }
54: // save the result of this pipe into the buffer
55: else {
56: printf("pipeline 2, pipe 0: input value = %d\n", buffer_1[pf.line()]);
57: buffer_2[pf.line()] = buffer_1[pf.line()];
58: }
59: }},
60:
61: tf::Pipe{tf::PipeType::PARALLEL, [&buffer_2](tf::Pipeflow& pf) {
62: printf(
63: "pipeline 2, pipe 1: input buffer_2[%zu] = %d\n",
64: pf.line(), buffer_2[pf.line()]
65: );
66: // propagate the previous result to this pipe by adding 1
67: buffer_2[pf.line()] = buffer_2[pf.line()] + 1;
68: }},
69:
70: tf::Pipe{tf::PipeType::SERIAL, [&buffer_2](tf::Pipeflow& pf) {
71: printf(
72: "pipeline 2, pipe 2: input buffer_2[%zu] = %d\n",
73: pf.line(), buffer_2[pf.line()]
74: );
75: // propagate the previous result to this pipe by adding 1
76: buffer_2[pf.line()] = buffer_2[pf.line()] + 1;
77: }}
78: );
79:
80: // build the pipeline graph using composition
81: tf::Task pipeline_1 = taskflow.composed_of(pl_1).name("pipeline_1");
82: tf::Task pipeline_2 = taskflow.composed_of(pl_2).name("pipeline_2");
83:
84: // specify the graph dependency
85: pipeline_1.precede(pipeline_2);
86:
87: // execute the taskflow
88: executor.run(taskflow).wait();
@endcode
Debrief:
@li Line 8 defines the data storage (@c num_lines integers) for pipeline @c pl_1
@li Line 9 defines the data storage (@c num_lines integers) for pipeline @c pl_2
@li Lines 14-24 define the first serial pipe in @c pl_1
@li Lines 26-33 define the second parallel pipe in @c pl_1
@li Lines 35-42 define the third serial pipe in @c pl_1
@li Lines 48-59 define the first serial pipe in @c pl_2 that takes the results of @c pl_1 as inputs
@li Lines 61-68 define the second parallel pipe in @c pl_2
@li Lines 70-77 define the third serial pipe in @c pl_2
@li Lines 81-82 define the pipeline graphs using composition
@li Line 85 defines the task dependency
@li Line 88 runs the taskflow
The taskflow graph of this pipeline example is illustrated as follows:
@dotfile images/pipeline_example2.dot
The following snippet shows one of the possible outputs:
@code{.bash}
pipeline 1, pipe 0: input token = 0
pipeline 1, pipe 1: input buffer_1[0] = 0
pipeline 1, pipe 2: input buffer_1[0] = 1
pipeline 1, pipe 0: input token = 1
pipeline 1, pipe 1: input buffer_1[1] = 1
pipeline 1, pipe 2: input buffer_1[1] = 2
pipeline 1, pipe 0: input token = 2
pipeline 1, pipe 1: input buffer_1[2] = 2
pipeline 1, pipe 2: input buffer_1[2] = 3
pipeline 1, pipe 0: input token = 3
pipeline 1, pipe 1: input buffer_1[3] = 3
pipeline 1, pipe 2: input buffer_1[3] = 4
pipeline 2, pipe 1: input value = 2
pipeline 2, pipe 2: input buffer_2[0] = 2
pipeline 2, pipe 3: input buffer_2[0] = 3
pipeline 2, pipe 1: input value = 3
pipeline 2, pipe 2: input buffer_2[1] = 3
pipeline 2, pipe 3: input buffer_2[1] = 4
pipeline 2, pipe 1: input value = 4
pipeline 2, pipe 2: input buffer_2[2] = 4
pipeline 2, pipe 3: input buffer_2[2] = 5
pipeline 2, pipe 1: input value = 5
pipeline 2, pipe 2: input buffer_2[3] = 5
pipeline 2, pipe 3: input buffer_2[3] = 6
@endcode
The output of pipelines @c pl_1 and @c pl_2 can be different from run to run
because their second pipes are both parallel types.
Due to the task dependency between @c pipeline_1 and @c pipeline_2,
the output of @c pl_1 precedes the output of @c pl_2.
@subsection DefineMultipleTaskParallelPipelines Example 3: Define Multiple Parallel Pipelines
This example creates two independent pipelines that run in parallel on different data sets.
@code{.cpp}
1: tf::Taskflow taskflow("pipeline");
2: tf::Executor executor;
3:
4: // define the maximum parallelism of the pipeline
5: const size_t num_lines = 4;
6:
7: // custom data storage
8: std::array<int, num_lines> buffer_1;
9: std::array<int, num_lines> buffer_2;
10:
11: // the pipeline_1 consists of three pipes (serial-parallel-serial)
12: // and up to four concurrent scheduling tokens
13: tf::Pipeline pl_1(num_lines,
14: tf::Pipe{tf::PipeType::SERIAL, [&buffer_1](tf::Pipeflow& pf) mutable{
15: // generate only 5 scheduling tokens
16: if(pf.token() == 5) {
17: pf.stop();
18: }
19: // save the result of this pipe into the buffer
20: else {
21: printf("pipeline 1, pipe 0: input token = %zu\n", pf.token());
22: buffer_1[pf.line()] = pf.token();
23: }
24: }},
25:
26: tf::Pipe{tf::PipeType::PARALLEL, [&buffer_1](tf::Pipeflow& pf) {
27: printf(
28: "pipeline 1, pipe 1: input buffer_1[%zu] = %d\n",
29: pf.line(), buffer_1[pf.line()]
30: );
31: // propagate the previous result to this pipe by adding one
32: buffer_1[pf.line()] = buffer_1[pf.line()] + 1;
33: }},
34:
35: tf::Pipe{tf::PipeType::SERIAL, [&buffer_1](tf::Pipeflow& pf) {
36: printf(
37: "pipeline 1, pipe 2: input buffer_1[%zu] = %d\n",
38: pf.line(), buffer_1[pf.line()]
39: );
40: // propagate the previous result to this pipe by adding one
41: buffer_1[pf.line()] = buffer_1[pf.line()] + 1;
42: }}
43: );
44:
45: // the pipeline_2 consists of three pipes (serial-parallel-serial)
46: // and up to four concurrent scheduling tokens
47: tf::Pipeline pl_2(num_lines,
48: tf::Pipe{tf::PipeType::SERIAL, [&buffer_2](tf::Pipeflow& pf) mutable{
49: // generate only 2 scheduling tokens
50: if(pf.token() == 5) {
51: pf.stop();
52: }
53: // save the result of this pipe into the buffer
54: else {
55: printf("pipeline 2, pipe 0: input token = %zu\n", pf.token());
56: buffer_2[pf.line()] = "pipeline";
57: }
58: }},
59:
60: tf::Pipe{tf::PipeType::PARALLEL, [&buffer_2](tf::Pipeflow& pf) {
61: printf(
62: "pipeline 2, pipe 1: input buffer_2[%zu] = %d\n",
63: pf.line(), buffer_2[pf.line()]
64: );
65: // propagate the previous result to this pipe by concatenating "_"
66: buffer_2[pf.line()] = buffer_2[pf.line()];
67: }},
68:
69: tf::Pipe{tf::PipeType::SERIAL, [&buffer_2](tf::Pipeflow& pf) {
70: printf(
71: "pipeline 2, pipe 2: input buffer_2[%zu] = %d\n",
72: pf.line(), buffer_2[pf.line()]
73: );
74: // propagate the previous result to this pipe by concatenating "2"
75: buffer_2[pf.line()] = buffer_2[pf.line()];
76: }}
77: );
78:
79: tf::Task pipeline_1 = taskflow.composed_of(pl_1)
80: .name("pipeline_1");
81: tf::Task pipeline_2 = taskflow.composed_of(pl_2)
82: .name("pipeline_2");
83: tf::Task initial = taskflow.emplace([](){ std::cout << "initial"; })
84: .name("initial");
85:
86: initial.precede(pipeline_1, pipeline_2);
87:
88: executor.run(taskflow).wait();
@endcode
Debrief:
@li Line 8 defines the data storage (@c num_lines integers) for pipeline @c pl_1
@li Line 9 defines the data storage (@c num_lines integers) for pipeline @c pl_2
@li Lines 14-24 define the first serial pipe in @c pl_1
@li Lines 26-33 define the second parallel pipe in @c pl_1
@li Lines 35-42 define the third serial pipe in @c pl_1
@li Lines 48-58 define the first serial pipe in @c pl_2
@li Lines 60-67 define the second parallel pipe in @c pl_2
@li Lines 69-76 define the third serial pipe in @c pl_2
@li Lines 79-82 define the pipeline graphs using composition
@li Lines 83-84 define a static task.
@li Line 86 defines the task dependency
@li Line 88 runs the taskflow
The taskflow graph of this pipeline example is illustrated as follows:
@dotfile images/pipeline_example3.dot
The following snippet shows one of the possible outputs:
@code{.bash}
initial
pipeline 2, pipe 0: input token = 0
pipeline 2, pipe 1: input buffer_2[0] = 0
pipeline 2, pipe 2: input buffer_2[0] = 1
pipeline 1, pipe 0: input token = 0
pipeline 1, pipe 1: input buffer_1[0] = 0
pipeline 1, pipe 2: input buffer_1[0] = 1
pipeline 1, pipe 0: input token = 1
pipeline 1, pipe 1: input buffer_1[1] = 1
pipeline 1, pipe 0: input token = 2
pipeline 1, pipe 1: input buffer_1[2] = 2
pipeline 1, pipe 0: input token = 3
pipeline 1, pipe 1: input buffer_1[3] = 3
pipeline 1, pipe 0: input token = 4
pipeline 1, pipe 1: input buffer_1[0] = 4
pipeline 2, pipe 0: input token = 1
pipeline 2, pipe 1: input buffer_2[1] = 1
pipeline 2, pipe 0: input buffer_2[1] = 2
pipeline 2, pipe 0: input token = 2
pipeline 2, pipe 1: input buffer_2[2] = 2
pipeline 2, pipe 2: input buffer_2[2] = 3
pipeline 2, pipe 0: input token = 3
pipeline 2, pipe 1: input buffer_2[3] = 3
pipeline 2, pipe 2: input buffer_2[3] = 4
pipeline 2, pipe 0: input token = 4
pipeline 2, pipe 1: input buffer_2[0] = 4
pipeline 2, pipe 2: input buffer_2[0] = 5
pipeline 1, pipe 2: input buffer_1[1] = 2
pipeline 1, pipe 2: input buffer_1[2] = 3
pipeline 1, pipe 2: input buffer_1[3] = 4
pipeline 1, pipe 2: input buffer_1[0] = 5
@endcode
Because pipeline @c pl_1 and pipeline @c pl_2 are running in parallel,
their outputs may interleave.
@section ResetPipeline Reset a Pipeline
Our pipeline scheduling framework keeps a @em stateful number of scheduled tokens
at each submitted run.
You can reset the pipeline to the initial state using tf::Pipeline::reset(),
where the number of scheduled tokens will start from zero in the next run.
Borrowed from @ref IterateAPipeline,
the program below resets the pipeline at the second iteration (inside the condition task)
so the scheduling token will start from zero in the next run.
@code{.cpp}
tf::Taskflow taskflow("pipeline");
tf::Executor executor;
// define the maximum parallelism of the pipeline
const size_t num_lines = 4;
// custom data storage
std::array<int, num_lines> buffer;
// the pipeline consists of three pipes (serial-parallel-serial)
// and up to four concurrent scheduling tokens
tf::Pipeline pl(num_lines,
tf::Pipe{tf::PipeType::SERIAL, [&buffer](tf::Pipeflow& pf) mutable{
// generate only 5 scheduling tokens
if(pf.token() == 5) {
pf.stop();
}
// save the result of this pipe into the buffer
else {
printf("pipe 0: input token = %zu\n", pf.token());
buffer[pf.line()] = pf.token();
}
}},
tf::Pipe{tf::PipeType::PARALLEL, [&buffer](tf::Pipeflow& pf) {
printf(
"pipe 1: input buffer_1[%zu] = %d\n", pf.line(), buffer[pf.line()]
);
// propagate the previous result to this pipe by adding one
buffer[pf.line()] = buffer[pf.line()] + 1;
}},
tf::Pipe{tf::PipeType::SERIAL, [&buffer](tf::Pipeflow& pf) {
printf(
"pipe 2: input buffer[%zu][%zu] = %d\n", pf.line(), buffer[pf.line()]
);
// propagate the previous result to this pipe by adding one
buffer[pf.line()] = buffer[pf.line()] + 1;
}}
);
tf::Task conditional = taskflow.emplace([&](){
if (++N < 2) {
pl.reset();
std::cout << "Rerun the pipeline\n";
return 0;
}
else {
return 1;
}
}).name("conditional");
tf::Task pipeline = taskflow.composed_of(pl)
.name("pipeline");
tf::Task initial = taskflow.emplace([](){ std::cout << "initial"; })
.name("initial");
tf::Task stop = taskflow.emplace([](){ std::cout << "stop"; })
.name("stop");
initial.precede(pipeline);
pipeline.precede(conditional);
conditional.precede(pipeline, stop);
executor.run(taskflow).wait();
@endcode
The following snippet shows one of the possible outputs:
@code{.bash}
initial
pipe 0: input token = 0
pipe 1: input buffer_1[0] = 0
pipe 2: input buffer_1[0] = 1
pipe 0: input token = 1
pipe 1: input buffer_1[1] = 1
pipe 2: input buffer_1[1] = 2
pipe 0: input token = 2
pipe 1: input buffer_1[2] = 2
pipe 2: input buffer_1[2] = 3
pipe 0: input token = 3
pipe 1: input buffer_1[3] = 3
pipe 2: input buffer_1[3] = 4
pipe 0: input token = 4
pipe 1: input buffer_1[0] = 4
pipe 2: input buffer_1[0] = 5
Rerun the pipeline
pipe 0: input token = 0
pipe 1: input buffer_1[0] = 0
pipe 2: input buffer_1[0] = 1
pipe 0: input token = 1
pipe 1: input buffer_1[1] = 1
pipe 2: input buffer_1[1] = 2
pipe 0: input token = 2
pipe 1: input buffer_1[2] = 2
pipe 2: input buffer_1[2] = 3
pipe 0: input token = 3
pipe 1: input buffer_1[3] = 3
pipe 2: input buffer_1[3] = 4
pipe 0: input token = 4
pipe 1: input buffer_1[0] = 4
pipe 2: input buffer_1[0] = 5
stop
@endcode
The output can be different from run to run, since the second pipe is a parallel type.
At the second iteration from the condition task,
we reset the pipeline so the token identifier starts from @c 0 rather than @c 5.
@section TaskParallelPipelineLearnMore Learn More about Taskflow Pipeline
Visit the following pages to learn more about pipeline:
+ @ref TaskParallelScalablePipeline
+ @ref DataParallelPipeline
+ @ref TextProcessingPipeline
+ @ref GraphProcessingPipeline
+ @ref TaskflowProcessingPipeline
*/
}
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