| 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 the execution of taskflow programs. TFProf records every task execution across all worker threads in every executor and produces either a compact binary trace file (``.tfp) for interactive visualization, or a concise text summary to standard error.

# Enable Taskflow Profiler

All taskflow programs include a lightweight, always-available profiling module. No recompilation or special build flags are needed. To activate it, set the environment variable `TF_ENABLE_PROFILER` to the desired output file path before running your program:

~$ TF_ENABLE_PROFILER=result.tfp ./my_taskflow

When the program finishes, it writes the profiling data to `result.tfp` in the TFProf binary format (``.tfp). If no file path is given (i.e., the variable is set but empty), TFProf prints a concise text summary to standard error instead.

# The .tfp Binary Format

The ``.tfp file is a compact binary format designed for fast loading and efficient compression. Each segment record stores:

- delta_beg : the time offset from the previous segment's start, encoded as a variable-length integer (varint). Because segments are time-ordered and adjacent tasks on the same worker tend to start close together, deltas are typically small (1–3 bytes rather than 8 bytes for an absolute timestamp).
- duration : the task execution time (`end` - `beg`), also varint-encoded.
- name_off, name_len : a reference into a per-executor string table that deduplicates task names.
- type|name_len : the task type and name length packed into a single byte.

A file with `N` executors contains one 12-byte file header followed by `N` self-contained executor blocks, each with its own string table. This design keeps the format simple and allows each executor block to be decoded independently. In practice, the delta + varint encoding reduces file size by 63–71% compared to a naive fixed-width representation — a 50 MB raw trace typically compresses to 15 MB or less without any external compression library.

# Visualize with the TFProf Web Interface

Open the TFProf web interface at https://taskflow.github.io/tfprof/ and drop your ``.tfp file onto the page (or click "Open .tfp"). The interface is a self-contained HTML file with no server, no installation, and no network dependency — it runs entirely in your browser.

The interface is organized into the following panels from top to bottom:

## Toolbar

The toolbar at the top shows:

- "Taskflow Profiler" — the application title.
- "Open .tfp" — opens a file picker to load a trace.
- "Reset Zoom" — returns to the full time range.
- "<- Back" — steps back to the previous zoom window.
- "Taskflow GitHub" — links to the Taskflow repository.

A statistics bar below the toolbar shows live summary values for the currently loaded trace and active zoom window: Workers, Tasks, Wall (total wall-clock duration), Window (current zoom range), and Visible (number of segments visible).

## Executor Filter

The "Executors: All N ▾" button opens a searchable popover listing every executor in the trace. Each row shows the executor ID alongside live statistics (task count, utilization, peak parallelism) drawn from the current zoom window, so you can immediately spot which executors are most active or most idle. All columns in the popover are sortable by clicking the column header — click once to sort descending, again to reverse. Selecting or deselecting executors instantly updates every panel below.

## Execution Timeline

The execution timeline is the main view. Each row represents one worker level (`E<i>.W<j>.L<k>` denotes executor `i`, physical worker `j`, nesting level `k`). A physical worker that spawns recursive subflows produces multiple levels; all levels share the same physical thread and are counted as one active worker.

Each colored segment represents a task execution, color-coded by task type:

Color | Type
--- | ---
Blue | Static task
Orange | Subflow task
Green | Condition task
Red/Pink | Async task
Gray | Clustered (multiple tasks merged for display)

When many tasks are too small to render individually at the current zoom level, TFProf merges adjacent tasks into a single clustered segment (shown in gray). Hover over any segment to see a tooltip with the task type, name, worker, duration, and start time. For clustered segments, the tooltip shows the task count and invites you to zoom in to see individual tasks.

Zooming: brush-select any horizontal region to zoom into that window. Double-click anywhere on the timeline to step back to the previous zoom level. The Reset Zoom button returns to the full trace.

The timeline uses virtual scrolling for large traces with thousands of workers — only the rows currently in the viewport are rendered, keeping the interface responsive regardless of worker count.

## Overview Minimap

The Overview panel below the timeline shows the entire trace compressed into a single minimap row per worker. A blue selection rectangle shows the current zoom window. Drag the selection to pan; brush a new region to jump there directly.

## Task Parallelism vs Active Workers

This section contains two stacked panels that share the same time axis:

- Task (top) — the number of tasks concurrently running at each point in time, drawn as a blue step-line area. This can exceed the physical worker count when subflow nesting produces multiple active tasks on the same thread.
- Worker (bottom) — the number of distinct physical workers simultaneously executing a task, drawn as a green filled area. Physical worker deduplication is applied: if worker `W1` is active at nesting levels `L0`, `L1`, and `L2` simultaneously, it is counted as one active worker.

Both panels zoom together with the main timeline. Brush the Worker panel to zoom, or double-click to step back.

## Critical Tasks

The "Critical Tasks" bar chart ranks the top-N tasks by duration within the current zoom window. The default is top 50; adjust the number with the input field. Bars are color-coded by task type. Hover a bar to see the task details; click it to zoom the timeline to that task's time span (with 50% padding on each side, clamped to the trace bounds).

## Executor Statistics

The "Executor Statistics" table reports per-executor metrics computed over the current zoom window:

Column | Meaning
--- | ---
Executor | Executor ID
Workers | Number of distinct physical worker threads
Tasks | Total task executions in the window
Wall Time | Length of the zoom window
Active Time | Union of all task intervals (time at least one worker was busy)
Idle Time | Wall Time − Active Time
Utilization | Σ(worker active time) / (Workers × Wall Time)
Peak | Maximum simultaneously active workers at any instant
Min Dur | Shortest individual task duration
Avg Dur | Mean task duration
Max Dur | Longest individual task duration

Click any column header to sort ascending or descending (indicated by ▲/▼). Utilization is color-coded: green ≥ 80%, amber 50–80%, red < 50%.

All values update live as you zoom or filter executors.

Notes printed below the table:

- Active Time is the union of all task intervals (not the sum of individual durations), so overlapping tasks on different workers are not double-counted.
- Utilization counts time a worker is executing a task on any nesting level — if `W1` runs at `L0` and `L1` simultaneously, only one unit of worker time is counted per wall-clock instant.

## Task Duration Distribution

The "Task Duration Distribution" panel shows the shape of the task duration distribution for the active executor selection and zoom window, drawn as a cyan step-line area plot.

The x-axis is the task duration and the y-axis is the task count per bin. TFProf automatically selects linear or logarithmic binning based on two signals:

1. Range ratio — if `max/min` > 50, the span is wide enough that linear bins would crush nearly all tasks into the leftmost few bins.
2. Skewness proxy — if the mean duration is more than 2× the median, the distribution is right-skewed (a few very long tasks dominate).

When both signals are present, log binning is used; otherwise linear binning is applied. Sub-nanosecond durations (below 1 ns = 0.001 µs) are clamped to the first bin; when this occurs the leftmost x-axis tick is labeled `<1ns`.

# Display Profile Summary to Console

To get a quick overview without opening the browser, set `TF_ENABLE_PROFILER` to an empty string. TFProf will print a text summary to standard error for each executor:

# enable the profiler without a file path to print summary to stderr
~$ TF_ENABLE_PROFILER= ./my_taskflow_program

A typical summary looks like this:

================================================================================
Observer 0 | Wall: 203.29 ms | Workers: 12 | Tasks: 45231 | Avg Utilization: 76.4%
================================================================================
[Aggregate Task Statistics]
------------------------------------------------------------------
Type Count Total(us) Avg(us) Min(us) Max(us)
------------------------------------------------------------------
static 44892 1823451 40.62 1.00 285.00
async 339 12483 36.82 2.00 197.00
[Worker Utilization]
----------------------------------------------------------------------------
Worker Tasks Busy(us) Idle(us) Avg(us) Min(us) Max(us) Util%
----------------------------------------------------------------------------
0 4821 155244 48049 32.20 1.0 285.0 76.4%
1 3902 148821 54472 38.14 1.0 241.0 73.2%
...
----------------------------------------------------------------------------
Total 45231 1823451 76.4% (avg)

The summary has three sections:

1. Overview — wall-clock duration, worker count, total task count, and average worker utilization. Utilization is the mean busy fraction across all workers; 100% means every worker was busy throughout the entire execution.
2. Aggregate Task Statistics — execution statistics broken down by task type. Columns report the execution count, total time, average, minimum, and maximum per-task duration.
3. Worker Utilization — per-worker breakdown listing task count, total busy time, idle time, average/min/max task duration, and per-worker utilization. Workers that ran no tasks are omitted. The `Total` row aggregates counts and times across all active workers.

# Profiling Tips

## Large Traces

For programs with millions of tasks the ``.tfp file can be tens of megabytes. TFProf loads and parses the file entirely in a background browser thread so the page remains responsive during loading. The execution timeline uses virtual scrolling so even traces with thousands of worker rows render smoothly.

## Recursive and Nested Subflows

Recursive taskflow programs (such as divide-and-conquer or Fibonacci-style graphs) produce many nesting levels per physical worker. The timeline labels these as `E<i>.W<j>.L<k>` where `L<k>` is the nesting depth. The Worker panel of the parallelism plot and the Executor Statistics table both deduplicate physical workers — if `W1` appears at levels `L0` through `L5` simultaneously, it counts as one active worker thread.

## Multiple Executors

When a program creates more than one `tf::Executor`, TFProf records each one as a separate executor block in the ``.tfp file. Use the Executor Filter to focus on a single executor or compare multiple executors side by side. The Executor Statistics table always shows one row per executor, making it easy to spot load imbalance across executors.

## Recommended Workflow

A typical profiling session follows this pattern:

1. Run your program with `TF_ENABLE_PROFILER=result.tfp`.
2. Open https://taskflow.github.io/tfprof/ and drop `result.tfp` onto the page.
3. Examine the Executor Statistics table to find executors with low utilization or high idle time — these are the first candidates for optimization.
4. Use the Executor Filter to isolate a single executor.
5. Look at the Task Parallelism panel — sustained low task count indicates sequential bottlenecks; high task count with low worker count indicates scheduling overhead.
6. Click a bar in the Critical Tasks chart to zoom the timeline to the longest-running task and inspect its neighbors.
7. Examine the Task Duration Distribution — a bimodal distribution (two peaks) suggests two qualitatively different task categories that may benefit from being separated into different executors or sized differently.