CPU Loader

CI Build Wheels Python Version License

A tool to generate CPU load on a system with runtime configuration through a WebUI and REST API.

✨ Key Features

  • 🎯 Precise Per-Thread Control: Set individual CPU load (0-100%) for each core independently
  • ⚡ High Performance: Native C implementation with pthreads ensures accurate load generation
  • 🧮 Configurable Algorithms: Choose between 5 different computation types (busy-wait, PI, primes, matrix, fibonacci)
  • ⏱️ Time-Controlled Execution: All algorithms respect precise timing for accurate load percentages
  • 📊 Real-Time Monitoring: Live WebSocket updates showing actual CPU usage and temperature via psutil
  • 🎛️ Interactive WebUI: Beautiful gradient interface with sliders and visual feedback
  • 🚀 REST API: Complete programmatic control for automation and testing
  • 📱 Responsive Design: Works seamlessly on desktop and mobile devices
  • 🔄 Instant Updates: Changes take effect immediately with sub-second response time
  • 🌡️ Temperature Monitoring: Optional CPU temperature tracking with sensor auto-detection
  • 📡 MQTT Integration: Publish metrics and settings to MQTT broker for IoT/monitoring systems

🖼️ Screenshots

Interface Screenshots

Initial view with zero load

Clean interface showing all CPU threads at idle

 Mobile responsive view

Fully responsive mobile interface
Mixed load across threads

Different load levels: 50%, 75%, and 25% on individual threads
Live CPU metrics updating

Real-time metrics showing actual CPU usage with color-coded bars

Features Shown

  • Visual Load Bars: Green progress bars show target load, blue bars display actual CPU usage
  • Real-Time Updates: WebSocket connection provides live metrics every second
  • Preset Buttons: Quick-set options (0%, 25%, 50%, 75%, 100%) for all threads
  • Smooth Gradients: Modern UI with purple-to-blue gradient design

Installation

Using uv tool (Recommended for CLI usage)

For users who want the cpu-loader command available system-wide, use uv tool install:

uv tool install cpu-loader

This installs cpu-loader as a standalone tool that can be run from anywhere:

cpu-loader --help
cpu-loader --host 0.0.0.0 --port 8000

From Pre-built Wheels

Pre-compiled wheels are available for Linux (x86_64, ARM64) and macOS (x86_64, ARM64):

pip install cpu-loader

From Source

  1. Clone the repository:
git clone https://github.com/the78mole/cpu-loader.git
cd cpu-loader
  1. Install dependencies and build C extension:
uv pip install -e .

This will compile the high-performance C extension for efficient CPU load generation.

  1. (Optional) Set up pre-commit hooks for development:
pre-commit install
  1. (Optional) Set up commit message template for semantic versioning:
git config commit.template .gitmessage

See COMMIT_MESSAGE_FORMAT.md for commit message guidelines.

Usage

Starting the Server

uv run src/main.py

The server will start on http://localhost:8000

Command-Line Options

uv run src/main.py --help

Available Options:

  • --host HOST: Host to bind the server to (default: 0.0.0.0)
  • --port PORT: Port to bind the server to (default: 8000)
  • --disable-temperature: Disable CPU temperature monitoring
  • --computation-type TYPE: Set computation algorithm (busy-wait, pi, primes, matrix, fibonacci)
  • --mqtt-broker-host HOST: MQTT broker hostname
  • --mqtt-broker-port PORT: MQTT broker port (default: 1883)
  • --mqtt-username USER: MQTT username
  • --mqtt-password PASS: MQTT password
  • --mqtt-topic-prefix PREFIX: MQTT topic prefix (default: cpu-loader)
  • --mqtt-client-id ID: MQTT client ID (default: cpu-loader)

Computation Types

CPU Loader supports different computation algorithms for load generation with precise time control:

  • busy-wait (default): Simple busy loop - fastest execution, minimal overhead, most accurate timing
  • pi: PI calculation using Leibniz formula - moderate computational intensity, mathematical workload
  • primes: Prime number finding - variable computational load, cryptographic-style operations
  • matrix: 4x4 matrix multiplication - consistent computational patterns, linear algebra operations
  • fibonacci: Lightweight mathematical operations - balanced computational load with micro-pauses

All algorithms are time-controlled to ensure accurate load percentages. The system uses 10ms cycles with frequent timing checks to maintain precise CPU utilization.

Examples:

# Use PI calculation for CPU load
uv run cpu-loader --computation-type pi

# Use prime number calculation
uv run cpu-loader --computation-type primes --port 8001

# Use matrix multiplication
uv run cpu-loader --computation-type matrix

WebUI

Open your browser and navigate to http://localhost:8000

Features:

  • Individual Thread Control: Use sliders to set load for each thread (0-100%)
  • Preset Buttons: Quick-set all threads to 0%, 10%, 25%, 50%, 80%, 90%, or 100%
  • Live Stats: View active thread count and average load
  • Real-time Updates: Changes are applied instantly with visual feedback

REST API

Get Thread Status

curl http://localhost:8000/api/threads

Response:

{
  "num_threads": 4,
  "loads": {
    "0": 0.0,
    "1": 0.0,
    "2": 0.0,
    "3": 0.0
  }
}

Set Load for Specific Thread

curl -X PUT http://localhost:8000/api/threads/0/load \
  -H "Content-Type: application/json" \
  -d '{"load_percent": 50.0}'

Set Load for All Threads

curl -X POST http://localhost:8000/api/threads/load/all \
  -H "Content-Type: application/json" \
  -d '{"load_percent": 75.0}'

Change Number of Threads

curl -X POST http://localhost:8000/api/threads \
  -H "Content-Type: application/json" \
  -d '{"num_threads": 8}'

Get Computation Type

curl http://localhost:8000/api/computation-type

Response:

{
  "computation_type": "pi",
  "available_types": ["busy-wait", "pi", "primes", "matrix", "fibonacci"]
}

Set Computation Type

curl -X PUT http://localhost:8000/api/computation-type \
  -H "Content-Type: application/json" \
  -d '{"computation_type": "fibonacci"}'

API Documentation

Once the server is running, visit http://localhost:8000/docs for interactive API documentation powered by Swagger UI.

MQTT Publishing

CPU Loader can publish real-time CPU metrics and load control settings to an MQTT broker for integration with home automation systems, monitoring tools, or custom applications.

MQTT Topics

The application publishes to two topics:

  1. {prefix}/cpu_metrics: Published every second with current CPU utilization

    {
  "total_cpu_percent": 25.5,
  "per_cpu_percent": [25.0, 26.0, 25.3, 25.7]
}

  2. {prefix}/load_settings: Published when load settings change (retained message)

    {
  "num_threads": 4,
  "loads": {"0": 25.0, "1": 25.0, "2": 25.0, "3": 25.0},
  "average_load": 25.0
}

Configuration

MQTT can be configured using environment variables or command-line arguments. Command-line arguments take precedence over environment variables.

Using Environment Variables

export MQTT_BROKER_HOST=mqtt.example.com
export MQTT_BROKER_PORT=1883
export MQTT_USERNAME=myuser
export MQTT_PASSWORD=mypassword
export MQTT_TOPIC_PREFIX=cpu-loader
export MQTT_CLIENT_ID=cpu-loader-001

uv run src/main.py

Using Command-Line Arguments

uv run src/main.py \
  --mqtt-broker-host mqtt.example.com \
  --mqtt-broker-port 1883 \
  --mqtt-username myuser \
  --mqtt-password mypassword \
  --mqtt-topic-prefix cpu-loader \
  --mqtt-client-id cpu-loader-001

Testing with Mosquitto

To test MQTT publishing locally:

# Install mosquitto
sudo apt-get install mosquitto mosquitto-clients

# Start CPU Loader with MQTT
uv run src/main.py --mqtt-broker-host localhost

# Subscribe to all topics (in another terminal)
mosquitto_sub -h localhost -t "cpu-loader/#" -v

MQTT Settings

Setting Environment Variable CLI Argument Default Description
Broker Host MQTT_BROKER_HOST --mqtt-broker-host None MQTT broker hostname or IP
Broker Port MQTT_BROKER_PORT --mqtt-broker-port 1883 MQTT broker port
Username MQTT_USERNAME --mqtt-username None MQTT authentication username
Password MQTT_PASSWORD --mqtt-password None MQTT authentication password
Topic Prefix MQTT_TOPIC_PREFIX --mqtt-topic-prefix cpu-loader Prefix for all MQTT topics
Client ID MQTT_CLIENT_ID --mqtt-client-id cpu-loader MQTT client identifier

Note: If no MQTT broker host is configured, MQTT publishing will be disabled and the application will function normally without it.

Example Script

An example script (src/example.py) is provided to demonstrate programmatic control:

# Start the server first
uv run src/main.py

# In another terminal, run the example
uv run src/example.py

The example demonstrates:

  • Getting current status
  • Setting all threads to a specific load
  • Gradually increasing load
  • Individual thread control
  • Resetting to idle

Computation Types Demo

A comprehensive demo script is available in examples/computation_types_demo.py to test all computation algorithms:

# Start the server
uv run cpu-loader

# In another terminal, run the demo (tests all computation types)
python examples/computation_types_demo.py

# Test specific computation types only
python examples/computation_types_demo.py --types pi fibonacci

# Custom load and duration
python examples/computation_types_demo.py --load 50 --duration 15

The demo script:

  • Tests each computation type systematically
  • Monitors actual CPU usage vs. target load
  • Provides performance comparisons between algorithms
  • Shows timing accuracy for each computation method

Running as a Systemd Service

You can run CPU Loader as a systemd service to start automatically on boot and run in the background.

Create Systemd Service File

Create /etc/systemd/system/cpu-loader.service:

[Unit]
Description=CPU Loader Service
After=network.target

[Service]
Type=simple
User=your-username
WorkingDirectory=/home/your-username
ExecStart=/home/your-username/.local/bin/cpu-loader --host 0.0.0.0 --port 8000
Restart=on-failure
RestartSec=5

# Optional: Set environment variables for MQTT
#Environment="MQTT_BROKER_HOST=mqtt.example.com"
#Environment="MQTT_BROKER_PORT=1883"
#Environment="MQTT_USERNAME=myuser"
#Environment="MQTT_PASSWORD=mypassword"
#Environment="MQTT_TOPIC_PREFIX=cpu-loader"
#Environment="MQTT_CLIENT_ID=cpu-loader-001"

# Optional: Disable temperature monitoring
#Environment="DISABLE_TEMPERATURE=1"

[Install]
WantedBy=multi-user.target

Important: Replace your-username with your actual username, and adjust the ExecStart path to point to where cpu-loader is installed:

  • If installed with uv tool install: Usually ~/.local/bin/cpu-loader
  • If installed with pip install --user: Usually ~/.local/bin/cpu-loader
  • If installed system-wide: Usually /usr/local/bin/cpu-loader

Enable and Start the Service

# Reload systemd to recognize the new service
sudo systemctl daemon-reload

# Enable the service to start on boot
sudo systemctl enable cpu-loader

# Start the service now
sudo systemctl start cpu-loader

# Check service status
sudo systemctl status cpu-loader

# View service logs
sudo journalctl -u cpu-loader -f

Manage the Service

# Stop the service
sudo systemctl stop cpu-loader

# Restart the service
sudo systemctl restart cpu-loader

# Disable auto-start on boot
sudo systemctl disable cpu-loader

Example Service Configurations

Basic Service (no MQTT):

[Unit]
Description=CPU Loader Service
After=network.target

[Service]
Type=simple
User=cpuloader
ExecStart=/home/cpuloader/.local/bin/cpu-loader --host 0.0.0.0 --port 8000
Restart=on-failure

[Install]
WantedBy=multi-user.target

Service with MQTT Integration:

[Unit]
Description=CPU Loader Service with MQTT
After=network.target

[Service]
Type=simple
User=cpuloader
ExecStart=/home/cpuloader/.local/bin/cpu-loader --host 0.0.0.0 --port 8000
Environment="MQTT_BROKER_HOST=192.168.1.100"
Environment="MQTT_BROKER_PORT=1883"
Environment="MQTT_USERNAME=cpuloader"
Environment="MQTT_PASSWORD=secretpassword"
Environment="MQTT_TOPIC_PREFIX=home/sensors/cpu-loader"
Restart=on-failure

[Install]
WantedBy=multi-user.target

Service with Custom Computation Type:

[Unit]
Description=CPU Loader Service (Matrix Computation)
After=network.target

[Service]
Type=simple
User=cpuloader
ExecStart=/home/cpuloader/.local/bin/cpu-loader --host 0.0.0.0 --port 8080 --computation-type matrix
Restart=on-failure

[Install]
WantedBy=multi-user.target

Architecture

  • src/cpu_loader_core.c: High-performance C implementation using pthreads for CPU load generation
  • src/cpu_loader.py: Python wrapper providing a clean API to the C extension
  • src/main.py: FastAPI application with REST API and embedded WebUI
  • src/mqtt_publisher.py: MQTT client for publishing metrics and settings
  • Threading Model: Native pthreads for maximum efficiency and precise timing
  • Load Algorithm: High-resolution busy-wait loops with nanosecond precision

Requirements

  • Python 3.8+
  • FastAPI 0.115.0+
  • Uvicorn 0.32.0+
  • Pydantic 2.10.0+
  • paho-mqtt 2.1.0+ (for MQTT publishing)
  • C compiler (gcc or clang) for building the extension

Use Cases

  • Performance Testing: Test application behavior under various CPU loads and computation types
  • Stress Testing: Validate system stability under high CPU utilization with different algorithms
  • Thermal Testing: Use fibonacci or matrix computations for maximum heat generation
  • Power Consumption Analysis: Compare power usage across different computation algorithms
  • Benchmarking: Create reproducible load scenarios with specific computation patterns
  • Algorithm Testing: Test cache performance (matrix), mathematical units (pi), or crypto operations (primes)
  • IoT Integration: Integrate with Home Assistant, Node-RED, or other MQTT-based systems
  • Monitoring: Feed CPU metrics and temperature data into monitoring dashboards via MQTT

Computation Algorithm Use Cases

  • busy-wait: Baseline testing, pure timing validation, minimal computational overhead
  • pi: Mathematical processing benchmarks, floating-point unit testing
  • primes: Cryptographic algorithm simulation, integer arithmetic testing
  • matrix: Linear algebra workloads, cache hierarchy testing, SIMD instruction testing
  • fibonacci: Balanced computational load for general stress testing

Development

Pre-commit Hooks

This project uses pre-commit hooks to ensure code quality. The hooks include:

  • Code Formatting: Black and isort for consistent Python formatting
  • Linting: Flake8 for code quality checks
  • Type Checking: Mypy for static type analysis
  • General Checks: Trailing whitespace, end-of-file fixes, YAML/JSON/TOML validation

To run pre-commit manually on all files:

pre-commit run --all-files

The hooks will run automatically on git commit after installation.

Building and Publishing Releases

The project uses automatic semantic versioning with GitHub Actions:

Versioning Rules

Versions are automatically determined based on commit messages:

  • Patch bump (0.0.X): Every commit to main

  • Minor bump (0.X.0): Commits prefixed with feat:

    git commit -m "feat: add new CPU monitoring feature"

  • Major bump (X.0.0): Commits with major:, breaking:, or BREAKING CHANGE:

    git commit -m "major: redesign API interface"
git commit -m "breaking: remove deprecated endpoints"

Release Process

  1. Commit and push to main:

    git add .
git commit -m "feat: add WebSocket support"
git push origin main

  2. Automated workflow:

    • Version is automatically calculated using semantic versioning
    • Wheels are built for Linux (x86_64, ARM64) and macOS (x86_64, ARM64)
    • Python versions: 3.8, 3.9, 3.10, 3.11, 3.12
    • Source distribution (sdist) is created
    • All artifacts are published to PyPI
    • A GitHub Release is created with version tag and artifacts

License

MIT License - see LICENSE file for details.

Project links

Data verified by PyPI on Jul 21, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

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  • Released:
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1 maintainer

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Avatar for the78mole from gravatar.com the78mole

Credits

Author: the78mole

GitHub Statistics

Data verified by PyPI on Jul 21, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

MIT

Requires

Python >=3.9

Tags

cpu load testing stress-test performance benchmark

Classifiers

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This release

0.8.5 This release

17 files

0.8.4

17 files

0.8.3

17 files

0.8.2

17 files

0.8.1

17 files

0.8.0

17 files

0.6.1

17 files

0.6.0

17 files

0.5.10

17 files

0.5.9

17 files

0.5.8

17 files

0.5.7

17 files

0.5.6

17 files

0.5.4

17 files

0.4.9

17 files

0.4.8

17 files

0.4.7

17 files

0.4.5

17 files

0.4.3

21 files

0.4.2

21 files

0.3.0

21 files

0.2.8

21 files

0.2.6

21 files

0.2.5

21 files

0.2.4

21 files

0.2.3

21 files

0.1.3

11 files

0.1.2

11 files

0.0.3

11 files

0.0.2

11 files

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