Parallel Pipeline
Contents
Taskflow provides a task-parallel pipeline programming framework for you to create a pipeline scheduling framework to implement pipeline algorithms. Pipeline parallelism refers to a parallel execution of multiple data elements through a linear chain of pipes or stages. Each stage processes the data element sent from the previous stage, applies the given callable to that data element, and then sends the result to the next stage. Multiple data elements can be processed simultaneously across different stages.
Include the Header
You need to include the header file, taskflow/algorithm/pipeline.hpp
, for creating a pipeline scheduling framework.
#include <taskflow/algorithm/pipeline.hpp>
Understand the Pipeline Scheduling Framework
A tf::
The figure above gives an example of our pipeline scheduling framework. The framework consists of three pipes (same concept as stages) and four lines (maximum parallelism). A pipeline of three pipes and four lines will propagate each data element through a sequential chain of three pipes and can simultaneously process up to four data elements on the four lines. Each edge represents a task dependency. For example, the edge from pipe-0
to pipe-1
in line 0
represents the task dependency between the first and the second pipes in the first line; the edge from pipe-0
in line 0
to pipe-0
in line 1
represents the task dependency between two adjacent lines when processing two data elements at the same pipe. Each pipe can be either a serial (tf::
Create a Pipeline Module Task
In general, there are three steps to create a pipeline application:
- define the pipeline structure (e.g., pipe type, pipe callable, stopping rule, line count)
- define the data storage and layout for the application
- define the pipeline taskflow graph using composition
The following code creates a pipeline scheduling framework for the example in the previous section. The framework schedules a total of five scheduling tokens labeled from 0 to 4. The first pipe stores the token identifier in a custom buffer, and each of the rest pipes adds one to the input data from the previous pipe and stores the result into the corresponding entry in the buffer.
1: tf::Taskflow taskflow; 2: tf::Executor executor; 3: 4: const size_t num_lines = 4; 5: const size_t num_pipes = 3; 6: 7: // custom data storage 8: std::array<std::array<int, num_pipes>, num_lines> mybuffer; 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, [&mybuffer](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: mybuffer[pf.line()][pf.pipe()] = pf.token(); 22: } 23: }}, 24: 25: tf::Pipe{tf::PipeType::PARALLEL, [&mybuffer](tf::Pipeflow& pf) { 26: printf( 27: "pipe 1: input mybuffer[%zu][%zu] = %d\n", 28: pf.line(), pf.pipe() - 1, mybuffer[pf.line()][pf.pipe() - 1] 29: ); 30: // propagate the previous result to this pipe by adding one 31: mybuffer[pf.line()][pf.pipe()] = mybuffer[pf.line()][pf.pipe()-1] + 1; 32: }}, 33: 34: tf::Pipe{tf::PipeType::SERIAL, [&mybuffer](tf::Pipeflow& pf) { 35: printf( 36: "pipe 2: input mybuffer[%zu][%zu] = %d\n", 37: pf.line(), pf.pipe() - 1, mybuffer[pf.line()][pf.pipe() - 1] 38: ); 39: // propagate the previous result to this pipe by adding one 40: mybuffer[pf.line()][pf.pipe()] = mybuffer[pf.line()][pf.pipe()-1] + 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();
Debrief:
- Lines 4-5 define the structure of the pipeline scheduling framework
- Line 8 defines the data storage as a two-dimensional array (
num_lines
bynum_pipes
) - Line 12 defines the number of lines in the pipeline
- Lines 13-23 define the first serial pipe, which will stop the pipeline scheduling at the fifth token
- Lines 25-32 define the second parallel pipe
- Lines 34-41 define the third serial pipe
- Line 45 defines the pipeline taskflow graph using composition
- Line 48 executes the taskflow
Taskflow leverages Runtime Tasking and Composable Tasking to implement the pipeline scheduling framework. The taskflow graph of this pipeline example is shown as follows:
In this example, we customize the data storage, mybuffer
, as a 4-by-3 array. The first dimension is equal to the number of lines (i.e., 4) and the second dimension is equal to the number of pipes (i.e., 3). Each data element in mybuffer
stores the data associated with the corresponding pipe in a line. For example, mybuffer[1][2]
stores the data processed in pipe-2
in line 1
. The following figure shows the data layout of mybuffer
.
For each scheduling token, you can use tf::3
and tf::2
(index starts from 0). The input value propagated from the previous pipe can be accessed at mybuffer[tf::Pipeflow::line()][tf::Pipeflow::pipe()-1]
. To stop the execution of the pipeline, we call tf::
Our pipeline algorithm schedules tokens in a cyclic manner, with a factor of num_lines
. That is, token t
will be processed in line t % num_lines
. The following snippet shows one of the possible outputs of this pipeline program:
pipe 0: input token = 0 pipe 1: input mybuffer[0][0] = 0 pipe 2: input mybuffer[0][1] = 1 pipe 0: input token = 1 pipe 1: input mybuffer[1][0] = 1 pipe 2: input mybuffer[1][1] = 2 pipe 0: input token = 2 pipe 1: input mybuffer[2][0] = 2 pipe 2: input mybuffer[2][1] = 3 pipe 0: input token = 3 pipe 1: input mybuffer[3][0] = 3 pipe 2: input mybuffer[3][1] = 4 pipe 0: input token = 4 pipe 1: input mybuffer[0][0] = 4 pipe 2: input mybuffer[0][1] = 5
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.
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:
- Example 1: Iterate a Pipeline
- Example 2: Concatenate Two Pipelines
- Example 3: Define Multiple Parallel Pipelines
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 once the next stream of data is available.
1: tf::Taskflow taskflow; 2: tf::Executor executor; 3: 4: const size_t num_lines = 4; 5: const size_t num_pipes = 3; 6: int i = 0, N = 0; 7: // custom data storage 8: std::array<std::array<int, num_pipes>, num_lines> mybuffer; 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, &mybuffer](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: mybuffer[pf.line()][pf.pipe()] = pf.token(); 22: } 23: }}, 24: 25: tf::Pipe{tf::PipeType::PARALLEL, [&mybuffer](tf::Pipeflow& pf) { 26: printf( 27: "stage 1: input mybuffer[%zu][%zu] = %d\n", 28: pf.line(), pf.pipe() - 1, mybuffer[pf.line()][pf.pipe() - 1] 29: ); 30: // propagate the previous result to this pipe by adding one 31: mybuffer[pf.line()][pf.pipe()] = mybuffer[pf.line()][pf.pipe()-1] + 1; 32: }}, 33: 34: tf::Pipe{tf::PipeType::SERIAL, [&mybuffer](tf::Pipeflow& pf) { 35: printf( 36: "stage 2: input mybuffer[%zu][%zu] = %d\n", 37: pf.line(), pf.pipe() - 1, mybuffer[pf.line()][pf.pipe() - 1] 38: ); 39: // propagate the previous result to this pipe by adding one 40: mybuffer[pf.line()][pf.pipe()] = mybuffer[pf.line()][pf.pipe()-1] + 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();
Debrief:
- Lines 4-5 define the structure of the pipeline scheduling framework
- Line 8 defines the data storage as a two-dimensional array (
num_lines
bynum_pipes
) - Line 12 defines the number of lines in the pipeline
- Lines 13-23 define the first serial pipe, which will stop the pipeline scheduling when
i
is5
- Lines 25-32 define the second parallel pipe
- Lines 34-41 define the third serial pipe
- Lines 44-53 define a condition task which returns 0 when
N
is less than2
, otherwise returns1
- Line 45 resets variable
i
- Lines 56-57 define the pipeline graph using composition
- Lines 58-61 define two static tasks
- Line 64-66 define the task dependency
- Line 69 executes the taskflow
The taskflow graph of this pipeline example is illustrated as follows:
The following snippet shows one of the possible outputs:
initial stage 0: input token = 0 stage 1: input mybuffer[0][0] = 0 stage 2: input mybuffer[0][1] = 1 stage 0: input token = 1 stage 1: input mybuffer[1][0] = 1 stage 2: input mybuffer[1][1] = 2 stage 0: input token = 2 stage 1: input mybuffer[2][0] = 2 stage 2: input mybuffer[2][1] = 3 stage 0: input token = 3 stage 1: input mybuffer[3][0] = 3 stage 2: input mybuffer[3][1] = 4 stage 0: input token = 4 stage 1: input mybuffer[0][0] = 4 stage 2: input mybuffer[0][1] = 5 Rerun the pipeline stage 0: input token = 5 stage 1: input mybuffer[1][0] = 5 stage 2: input mybuffer[1][1] = 6 stage 0: input token = 6 stage 1: input mybuffer[2][0] = 6 stage 2: input mybuffer[2][1] = 7 stage 0: input token = 7 stage 1: input mybuffer[3][0] = 7 stage 2: input mybuffer[3][1] = 8 stage 0: input token = 8 stage 1: input mybuffer[0][0] = 8 stage 2: input mybuffer[0][1] = 9 stage 0: input token = 9 stage 1: input mybuffer[1][0] = 9 stage 2: input mybuffer[1][1] = 10 stop
The pipeline runs twice as controlled by the condition task conditional
. The starting token in the second run of the pipeline is 5
rather than 0
because the pipeline keeps a stateful number of tokens. The last token is 9
, which means the pipeline processes in total 10
scheduling tokens. The first five tokens (token 0
to 4
) are processed in the first run, and the remaining five tokens (token 5
to 9
) are processed in the second run. In the condition task, we use N
as a decision-making counter to process the next stream of data.
Example 2: Concatenate Two Pipelines
This example demonstrates two concatenated pipelines where a sequence of data elements run synchronously from one pipeline to another pipeline. The first pipeline task precedes the second pipeline task.
1: tf::Taskflow taskflow("pipeline"); 2: tf::Executor executor; 3: 4: const size_t num_lines = 4; 5: const size_t num_pipes = 3; 6: 7: // custom data storage 8: std::array<std::array<int, num_pipes>, num_lines> mybuffer_1; 9: std::array<std::array<int, num_pipes>, num_lines> mybuffer_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, [&mybuffer_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: mybuffer_1[pf.line()][pf.pipe()] = pf.token(); 23: } 24: }}, 25: 26: tf::Pipe{tf::PipeType::PARALLEL, [&mybuffer_1](tf::Pipeflow& pf) { 27: printf( 28: "pipeline 1, pipe 1: input mybuffer_1[%zu][%zu] = %d\n", 29: pf.line(), pf.pipe() - 1, mybuffer_1[pf.line()][pf.pipe() - 1] 30: ); 31: // propagate the previous result to this pipe by adding one 32: mybuffer_1[pf.line()][pf.pipe()] = mybuffer_1[pf.line()][pf.pipe()-1] + 1; 33: }}, 34: 35: tf::Pipe{tf::PipeType::SERIAL, [&mybuffer_1](tf::Pipeflow& pf) { 36: printf( 37: "pipeline 1, pipe 2: input mybuffer_1[%zu][%zu] = %d\n", 38: pf.line(), pf.pipe() - 1, mybuffer_1[pf.line()][pf.pipe() - 1] 39: ); 40: // propagate the previous result to this pipe by adding one 41: mybuffer_1[pf.line()][pf.pipe()] = mybuffer_1[pf.line()][pf.pipe()-1] + 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: [&mybuffer_2, &mybuffer_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", mybuffer_1[pf.line()][2]); 57: mybuffer_2[pf.line()][pf.pipe()] = mybuffer_1[pf.line()][2]; 58: } 59: }}, 60: 61: tf::Pipe{tf::PipeType::PARALLEL, [&mybuffer_2](tf::Pipeflow& pf) { 62: printf( 63: "pipeline 2, pipe 1: input mybuffer_2[%zu][%zu] = %d\n", 64: pf.line(), pf.pipe() - 1, mybuffer_2[pf.line()][pf.pipe() - 1] 65: ); 66: // propagate the previous result to this pipe by adding 1 67: mybuffer_2[pf.line()][pf.pipe()] = mybuffer_2[pf.line()][pf.pipe()-1] + 1; 68: }}, 69: 70: tf::Pipe{tf::PipeType::SERIAL, [&mybuffer_2](tf::Pipeflow& pf) { 71: printf( 72: "pipeline 2, pipe 2: input mybuffer_2[%zu][%zu] = %d\n", 73: pf.line(), pf.pipe() - 1, mybuffer_2[pf.line()][pf.pipe() - 1] 74: ); 75: // propagate the previous result to this pipe by adding 1 76: mybuffer_2[pf.line()][pf.pipe()] = mybuffer_2[pf.line()][pf.pipe()-1] + 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();
Debrief:
- Line 8 defines the data storage (
num_lines
bynum_pipes
) for pipelinepl_1
- Line 9 defines the data storage (
num_lines
bynum_pipes
) for pipelinepl_2
- Lines 14-24 define the first serial pipe in
pl_1
- Lines 26-33 define the second parallel pipe in
pl_1
- Lines 35-42 define the third serial pipe in
pl_1
- Lines 48-59 define the first serial pipe in
pl_2
that takes the results ofpl_1
as inputs - Lines 61-68 define the second parallel pipe in
pl_2
- Lines 70-77 define the third serial pipe in
pl_2
- Lines 81-82 define the pipeline graphs using composition
- Line 85 defines the task dependency
- Line 88 runs the taskflow
The taskflow graph of this pipeline example is illustrated as follows:
The following snippet shows one of the possible outputs:
pipeline 1, pipe 0: input token = 0 pipeline 1, pipe 1: input mybuffer_1[0][0] = 0 pipeline 1, pipe 2: input mybuffer_1[0][1] = 1 pipeline 1, pipe 0: input token = 1 pipeline 1, pipe 1: input mybuffer_1[1][0] = 1 pipeline 1, pipe 2: input mybuffer_1[1][1] = 2 pipeline 1, pipe 0: input token = 2 pipeline 1, pipe 1: input mybuffer_1[2][0] = 2 pipeline 1, pipe 2: input mybuffer_1[2][1] = 3 pipeline 1, pipe 0: input token = 3 pipeline 1, pipe 1: input mybuffer_1[3][0] = 3 pipeline 1, pipe 2: input mybuffer_1[3][1] = 4 pipeline 2, pipe 1: input value = 2 pipeline 2, pipe 2: input mybuffer_2[0][0] = 2 pipeline 2, pipe 3: input mybuffer_2[0][1] = 3 pipeline 2, pipe 1: input value = 3 pipeline 2, pipe 2: input mybuffer_2[1][0] = 3 pipeline 2, pipe 3: input mybuffer_2[1][1] = 4 pipeline 2, pipe 1: input value = 4 pipeline 2, pipe 2: input mybuffer_2[2][0] = 4 pipeline 2, pipe 3: input mybuffer_2[2][1] = 5 pipeline 2, pipe 1: input value = 5 pipeline 2, pipe 2: input mybuffer_2[3][0] = 5 pipeline 2, pipe 3: input mybuffer_2[3][1] = 6
The output of pipelines pl_1
and pl_2
can be different from run to run because their second pipes are both parallel types. Due to the task dependency between pipeline_1
and pipeline_2
, the output of pl_1
precedes the output of pl_2
.
Example 3: Define Multiple Parallel Pipelines
This example creates two independent pipelines that run in parallel on different data sets.
1: tf::Taskflow taskflow("pipeline"); 2: tf::Executor executor; 3: 4: const size_t num_lines = 4; 5: const size_t num_pipes = 3; 6: 7: // custom data storage 8: std::array<std::array<int, num_pipes>, num_lines> mybuffer_1; 9: std::array<std::array<int, num_pipes>, num_lines> mybuffer_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, [&mybuffer_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: mybuffer_1[pf.line()][pf.pipe()] = pf.token(); 23: } 24: }}, 25: 26: tf::Pipe{tf::PipeType::PARALLEL, [&mybuffer_1](tf::Pipeflow& pf) { 27: printf( 28: "pipeline 1, pipe 1: input mybuffer_1[%zu][%zu] = %d\n", 29: pf.line(), pf.pipe() - 1, mybuffer_1[pf.line()][pf.pipe() - 1] 30: ); 31: // propagate the previous result to this pipe by adding one 32: mybuffer_1[pf.line()][pf.pipe()] = mybuffer_1[pf.line()][pf.pipe()-1] + 1; 33: }}, 34: 35: tf::Pipe{tf::PipeType::SERIAL, [&mybuffer_1](tf::Pipeflow& pf) { 36: printf( 37: "pipeline 1, pipe 2: input mybuffer_1[%zu][%zu] = %d\n", 38: pf.line(), pf.pipe() - 1, mybuffer_1[pf.line()][pf.pipe() - 1] 39: ); 40: // propagate the previous result to this pipe by adding one 41: mybuffer_1[pf.line()][pf.pipe()] = mybuffer_1[pf.line()][pf.pipe()-1] + 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, [&mybuffer_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: mybuffer_2[pf.line()][pf.pipe()] = "pipeline"; 57: } 58: }}, 59: 60: tf::Pipe{tf::PipeType::PARALLEL, [&mybuffer_2](tf::Pipeflow& pf) { 61: printf( 62: "pipeline 2, pipe 1: input mybuffer_2[%zu][%zu] = %d\n", 63: pf.line(), pf.pipe() - 1, mybuffer_2[pf.line()][pf.pipe() - 1] 64: ); 65: // propagate the previous result to this pipe by concatenating "_" 66: mybuffer_2[pf.line()][pf.pipe()] = mybuffer_2[pf.line()][pf.pipe()-1]; 67: }}, 68: 69: tf::Pipe{tf::PipeType::SERIAL, [&mybuffer_2](tf::Pipeflow& pf) { 70: printf( 71: "pipeline 2, pipe 2: input mybuffer_2[%zu][%zu] = %d\n", 72: pf.line(), pf.pipe() - 1, mybuffer_2[pf.line()][pf.pipe() - 1] 73: ); 74: // propagate the previous result to this pipe by concatenating "2" 75: mybuffer_2[pf.line()][pf.pipe()] = mybuffer_2[pf.line()][pf.pipe()-1]; 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();
Debrief:
- Line 8 defines the data storage (
num_lines
bynum_pipes
) for pipelinepl_1
- Line 9 defines the data storage (
num_lines
bynum_pipes
) for pipelinepl_2
- Lines 14-24 define the first serial pipe in
pl_1
- Lines 26-33 define the second parallel pipe in
pl_1
- Lines 35-42 define the third serial pipe in
pl_1
- Lines 48-58 define the first serial pipe in
pl_2
- Lines 60-67 define the second parallel pipe in
pl_2
- Lines 69-76 define the third serial pipe in
pl_2
- Lines 79-82 define the pipeline graphs using composition
- Lines 83-84 define a static task.
- Line 86 defines the task dependency
- Line 88 runs the taskflow
The taskflow graph of this pipeline example is illustrated as follows:
The following snippet shows one of the possible outputs:
initial pipeline 2, pipe 0: input token = 0 pipeline 2, pipe 1: input mybuffer_2[0][0] = 0 pipeline 2, pipe 2: input mybuffer_2[0][1] = 1 pipeline 1, pipe 0: input token = 0 pipeline 1, pipe 1: input mybuffer_1[0][0] = 0 pipeline 1, pipe 2: input mybuffer_1[0][1] = 1 pipeline 1, pipe 0: input token = 1 pipeline 1, pipe 1: input mybuffer_1[1][0] = 1 pipeline 1, pipe 0: input token = 2 pipeline 1, pipe 1: input mybuffer_1[2][0] = 2 pipeline 1, pipe 0: input token = 3 pipeline 1, pipe 1: input mybuffer_1[3][0] = 3 pipeline 1, pipe 0: input token = 4 pipeline 1, pipe 1: input mybuffer_1[0][0] = 4 pipeline 2, pipe 0: input token = 1 pipeline 2, pipe 1: input mybuffer_2[1][0] = 1 pipeline 2, pipe 0: input mybuffer_2[1][1] = 2 pipeline 2, pipe 0: input token = 2 pipeline 2, pipe 1: input mybuffer_2[2][0] = 2 pipeline 2, pipe 2: input mybuffer_2[2][1] = 3 pipeline 2, pipe 0: input token = 3 pipeline 2, pipe 1: input mybuffer_2[3][0] = 3 pipeline 2, pipe 2: input mybuffer_2[3][1] = 4 pipeline 2, pipe 0: input token = 4 pipeline 2, pipe 1: input mybuffer_2[0][0] = 4 pipeline 2, pipe 2: input mybuffer_2[0][1] = 5 pipeline 1, pipe 2: input mybuffer_1[1][1] = 2 pipeline 1, pipe 2: input mybuffer_1[2][1] = 3 pipeline 1, pipe 2: input mybuffer_1[3][1] = 4 pipeline 1, pipe 2: input mybuffer_1[0][1] = 5
Because pipeline pl_1
and pipeline pl_2
are running in parallel, their outputs may interleave.
Reset a Pipeline
Our pipeline scheduling framework keeps a stateful number of scheduled tokens at each run submitted to an executor. You can reset the pipeline to the initial state using tf::
1: tf::Taskflow taskflow("pipeline"); 2: tf::Executor executor; 3: 4: const size_t num_lines = 4; 5: const size_t num_pipes = 3; 6: 7: // custom data storage 8: std::array<std::array<int, num_pipes>, num_lines> mybuffer; 9: 10: 11: // the pipeline consists of three pipes (serial-parallel-serial) 12: // and up to four concurrent scheduling tokens 13: tf::Pipeline pl(num_lines, 14: tf::Pipe{tf::PipeType::SERIAL, [&mybuffer](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("pipe 0: input token = %zu\n", pf.token()); 22: mybuffer[pf.line()][pf.pipe()] = pf.token(); 23: } 24: }}, 25: 26: tf::Pipe{tf::PipeType::PARALLEL, [&mybuffer](tf::Pipeflow& pf) { 27: printf( 28: "pipe 1: input mybuffer_1[%zu][%zu] = %d\n", 29: pf.line(), pf.pipe() - 1, mybuffer[pf.line()][pf.pipe() - 1] 30: ); 31: // propagate the previous result to this pipe by adding one 32: mybuffer[pf.line()][pf.pipe()] = mybuffer[pf.line()][pf.pipe()-1] + 1; 33: }}, 34: 35: tf::Pipe{tf::PipeType::SERIAL, [&mybuffer](tf::Pipeflow& pf) { 36: printf( 37: "pipe 2: input mybuffer[%zu][%zu] = %d\n", 38: pf.line(), pf.pipe() - 1, mybuffer[pf.line()][pf.pipe() - 1] 39: ); 40: // propagate the previous result to this pipe by adding one 41: mybuffer[pf.line()][pf.pipe()] = mybuffer[pf.line()][pf.pipe()-1] + 1; 42: }} 43: ); 44: 45: tf::Task conditional = taskflow.emplace([&](){ 46: if (++N < 2) { 47: pl.reset(); 48: std::cout << "Rerun the pipeline\n"; 49: return 0; 50: } 51: else { 52: return 1; 53: } 54: }).name("conditional"); 55: 56: tf::Task pipeline = taskflow.composed_of(pl) 57: .name("pipeline"); 58: tf::Task initial = taskflow.emplace([](){ std::cout << "initial"; }) 59: .name("initial"); 60: tf::Task stop = taskflow.emplace([](){ std::cout << "stop"; }) 61: .name("stop"); 62: 63: initial.precede(pipeline); 64: pipeline.precede(conditional); 65: conditional.precede(pipeline, stop); 66: 67: executor.run(taskflow).wait();
The following snippet shows one of the possible outputs:
initial pipe 0: input token = 0 pipe 1: input mybuffer_1[0][0] = 0 pipe 2: input mybuffer_1[0][1] = 1 pipe 0: input token = 1 pipe 1: input mybuffer_1[1][0] = 1 pipe 2: input mybuffer_1[1][1] = 2 pipe 0: input token = 2 pipe 1: input mybuffer_1[2][0] = 2 pipe 2: input mybuffer_1[2][1] = 3 pipe 0: input token = 3 pipe 1: input mybuffer_1[3][0] = 3 pipe 2: input mybuffer_1[3][1] = 4 pipe 0: input token = 4 pipe 1: input mybuffer_1[0][0] = 4 pipe 2: input mybuffer_1[0][1] = 5 Rerun the pipeline pipe 0: input token = 0 pipe 1: input mybuffer_1[0][0] = 0 pipe 2: input mybuffer_1[0][1] = 1 pipe 0: input token = 1 pipe 1: input mybuffer_1[1][0] = 1 pipe 2: input mybuffer_1[1][1] = 2 pipe 0: input token = 2 pipe 1: input mybuffer_1[2][0] = 2 pipe 2: input mybuffer_1[2][1] = 3 pipe 0: input token = 3 pipe 1: input mybuffer_1[3][0] = 3 pipe 2: input mybuffer_1[3][1] = 4 pipe 0: input token = 4 pipe 1: input mybuffer_1[0][0] = 4 pipe 2: input mybuffer_1[0][1] = 5 stop
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 0
rather than 5
.