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

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Profile Taskflow Programs

Taskflow comes with a built-in profiler, TFProf, for you to profile and visualize taskflow programs.

# Enable Taskflow Profiler

All taskflow programs come with a lightweight profiling module to observer worker activities in every executor. To enable the profiler, set the environment variable `TF_ENABLE_PROFILER` to a file name in which the profiling result will be stored.

~$ TF_ENABLE_PROFILER=result.json ./my_taskflow
~$ cat result.json
[
{"executor":"0","data":[{"worker":12,"level":0,"data":[{"span":[72,117],"name":"12_0","type":"static"},{"span":[121,123],"name":"12_1","type":"static"},{"span":[123,125],"name":"12_2","type":"static"},{"span":[125,127],"name":"12_3","type":"static"}]}]}
]

When the program finishes, it generates and saves the profiling data to `result.json` in JavaScript Object Notation (JSON) format. You can then paste the JSON data to our web-based interface, Taskflow Profiler, to visualize the execution timelines of tasks and workers. The web interface supports the following features:

- zoom into a selected window
- double click to zoom back to the previously selected window
- filter workers
- mouse over to show the tooltip of the task
- rank tasks in decreasing order of criticality (i.e., execution time)

TFProf implements a clustering-based algorithm to efficiently visualize tasks and their execution timelines in a browser. Without losing much visual accuracy, each clustered task indicates a group of adjacent tasks clustered by the algorithm, and you can zoom in to see these tasks.

# Enable Taskflow Profiler on a HTTP Server

When profiling large taskflow programs, the method in the previous section may not work because of the limitation of processing large JSON files. For example, a taskflow program of a million tasks can produce several GBs of profiling data, and the profile may respond to your requests very slowly. To solve this problem, we have implemented a C++-based http server optimized for our profiling data. To compile the server, enable the cmake option `TF_BUILD_PROFILER`. You may visit Building and Installing to understand Taskflow's build environment.

# under the build directory
~$ cmake ../ -DTF_BUILD_PROFILER=ON
~$ make

After successfully compiling the server, you can find the executable at `tfprof/server/tfprof`. Now, generate profiling data from running a taskflow program but specify the output file with extension ``.tfp.

~$ TF_ENABLE_PROFILER=my_taskflow.tfp ./my_taskflow
~$ ls
my_taskflow.tfp # my_taskflow.tfp is of binary format

Launch the server program `tfprof/server/tfprof` and pass (1) the directory of `index.html` (default at `tfprof/`) via the option `--mount` and (2) the `my_taskflow.tfp` via the option `--input`.

# under the build/ directory
~$ ./tfprof/server/tfprof --mount ../tfprof/ --input my_taskflow.tfp

Now, open your favorite browser at `localhost:8080` to visualize and profile your `my_taskflow` program.

The compiled profiler is a more powerful version than the pure JavaScript-based interface and it is able to more efficiently handle large profiling data under different queries. We currently support the following two view types:

- Cluster: visualize the profiling data using a clustering algorithm with a limit
- Criticality: visualize the top-limit tasks in decreasing order of their execution times

# Display Profile Summary

You can display a profile summary by specifying only the environment variable `TF_ENABLE_PROFILER` without any value. The Taskflow will generate a separate summary report of tasks and workers for each executor created by the program.

# enable the environment variable without any value
~$ TF_ENABLE_PROFILER= ./my_taskflow_program
# your program output
...
...
...
# Taskflow profile summary
==Observer 0: 1 workers completed 18 tasks in 28 us
-Task- Count Time (us) Avg (us) Min (us) Max (us)
static 7 5 0.714286 0 4
condition 11 0 0.000000 0 0
-Worker- Level Task Count Time (us) Avg (us) Min (us) Max (us)
14 0 static 7 5 0.714286 0 4
condition 11 0 0.000000 0 0
18 5 0.277778 0 4

The report consists of two sections, task summary and worker summary. In the first section, the summary reports for each task type the number of executions (`Count`), the total execution time (`Time`), average execution time per task (`Avg`), and the minimum (`Min`) and the maximum (`Max`) execution time among all tasks. Similarly in the second section, the summary reports for each worker the task execution statistics.