⚡ ComputeX

ComputeX is a high-performance, distributed compute engine built to solve complex computational problems fast, scalably, and modularly. From matrix multiplication and large-scale sorting to word frequency analysis, image processing, and prime number operations — ComputeX distributes the workload across multiple worker nodes using TCP sockets and a Flask-based API, paired with a sleek React frontend.

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Key Features

• High-Speed Problem Solving
  Parallelized execution of compute-heavy tasks using multiprocessing and distributed data splits.

• Distributed Architecture
  Worker nodes run in parallel over TCP, ideal for handling large datasets and high throughput tasks.

• Modular + Extensible Design
  Plug-and-play architecture to easily add new task modules (Fibonacci, AI inference, file compression, etc).

• Fault Tolerance (Resilient by Design)
  Automatically detects and redistributes tasks if a worker crashes — no need for manual restarts.

• Lightweight Deployment (No Broker)
  Zero-dependency communication between master and workers — no Kafka, RabbitMQ, or Celery required.

• RESTful Flask API
  Clean, scalable REST endpoints for all task types — easily consumable by any client or frontend.

• Responsive Web Interface (React)
  Interactive React frontend with task configuration, real-time output, file upload, and result visualization.

• Market-Ready Architecture
  Designed to align with current tech trends like:

  • Distributed computing for edge/cloud tasks
  • AI/ML backend extensions
  • Serverless microservices
  • Scalable compute APIs (internal tooling, dev platforms, etc.)

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✅ Ideal for:

• Data engineering experiments
• Educational visualization of distributed systems
• Cloud-native compute backend prototypes
• Real-time task execution tools
• Hackathons, college projects, and system design showcases

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DEMO VIDEO

ComputeX_v1.0.3.mp4

Technology Stack

• Python 3.11+
• Flask (REST API)
• React.js (Frontend)
• TCP Sockets (Distributed Workers)
• Multiprocessing
• Pillow, pandas, numpy, python-docx for task-specific processing

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Architecture Overview

               [ User Frontend (React) ]
                        │
                 [ Flask API Server ]
                        │
     ┌───────────┬───────────┬───────────┐
     │           │           │           │
 [Worker 1]  [Worker 2]  [Worker 3]  ... [Worker N]
     │           │           │           │
     -------------------------------------
                        ↓
             Aggregated Task Result

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Project Structure

ComputeX/
├── app.py
├── routes.py
├── uploads/
├── tasks/
│   ├── word_freq.py
│   ├── sort_list.py
│   ├── matrix_multiply.py
│   ├── image_grayscale.py
│   ├── factorial_distributed.py
│   ├── fibonacci_distributed.py
│   └── prime_distributed.py
├── utils/
├── frontend/
│   └── src/components/
├── requirements.txt
├── Dockerfile
├── docker-compose.yml
└── README.md

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Supported Tasks

Task	Description	Input Format
Word Frequency	Counts word occurrences	.txt, .csv, .docx
Sort List	Sorts large numeric datasets	.csv, .txt
Matrix Multiplication	Multiplies two matrices	JSON array
Image Grayscale	Converts image to grayscale	.jpg, .png
Factorial Generator	Computes factorials	Integer input
Fibonacci Generator	Generates Fibonacci sequence	Integer input
Prime Checker	Checks if a number is prime	Integer input
Prime Generator	Generates primes ≤ N	Integer input

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Installation

Backend Setup (Python + Flask)

git clone https://github.com/your-org/distributed-task-system.git
cd distributed-task-system

python3 -m venv .venv
source .venv/bin/activate

pip install -r requirements.txt
python app.py

Frontend Setup (React)

cd frontend
npm install
npm run dev

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Running Workers

Each task file includes a worker that can be launched independently:

python -c "from tasks.sort_list import run_sort_worker; run_sort_worker(6001)"

Or run from a main block:

if __name__ == "__main__":
    run_worker(6001)

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API Examples

Matrix Multiplication

POST /multiply

{
  "matrixA": [[1, 2], [3, 4]],
  "matrixB": [[5, 6], [7, 8]]
}

Word Frequency

POST /api/process
Upload file (.txt, .docx, or .csv) as multipart/form-data with key file.

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Docker Support

Build and run containers using Docker Compose:

docker-compose build
docker-compose up

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Adding a New Task

1. Create a new Python file under tasks/
2. Define run_worker, send_to_worker, and task-specific logic
3. Add an API route in routes.py
4. Update frontend if required

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Contributing

1. Fork the repository
2. Create a feature branch: git checkout -b feat/your-feature
3. Commit changes and push: git push origin feat/your-feature
4. Open a pull request for review

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License

MIT License
© 2025 Sunny Kumar Pandit and contributors

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