Taskflow
2.7.0
|
Cpp-Taskflow 2.2.0 is the 3rd release in the 2.x line! This release includes several new changes such as tf::ExecutorObserverInterface, tf::Executor, isolation of taskflow graph and executor, benchmarks, and so forth. In particular, this release improve the performance of the work stealing scheduler.
Cpp-Taskflow 2.2.0 can be downloaded from here.
In this release, we isolated the executor interface from tf::Taskflow, and merge tf::Framework with tf::Taskflow. This change largely improved the modularity and composability of Cpp-Taskflow in creating clean task dependency graphs and execution flows. Performance is also better. While this introduced some breaks in tf::Taskflow, we have managed to make it as less painful as possible for users to adapt to the new change.
Previously, tf::Taskflow is a hero class that manages both a task dependency graph and the execution of all graphs including frameworks. For example:
However, this design is awkward in many aspects. For instance, calling wait_for_all
dispatches the present graph and the graph vanishes when the execution completes. To reuse a graph, users have to create another special graph called framework and mix its execution with the one in a taskflow object. Given the user feedback and lessons we have learned so far, we decided to isolate the executor interface out of tf::Taskflow and merge tf::Framework with tf::Taskflow. All execution methods such as dispatch
and wait_for_all
have been moved from tf::Taskflow to tf::Executor.
The new design has a clean separation between a task dependency graph builder tf::Taskflow and the execution of graphs tf::Executor. Users are fully responsible for the lifetime of a taskflow, and need to ensure a taskflow is alive during its execution. Besides, all task constructs remain unchanged in tf::Taskflow. In most situations, you will just need to add an executor to your program to run your taskflow graphs.
Again, we apologize this breaking change! I hope you can understand what we did is to make Cpp-Taskflow provide good performance scaling and user experience.