# Sequential vs Parallel Processing

Demonstrates how AsyncParallelBatchNode accelerates processing by 3x over AsyncBatchNode.

## Features

- Processes identical tasks with two approaches
- Compares sequential vs parallel execution time
- Shows 3x speed improvement with parallel processing

## Run It

```bash
pip install pocketflow
python main.py
```

## Output

```
=== Running Sequential (AsyncBatchNode) ===
[Sequential] Summarizing file1.txt...
[Sequential] Summarizing file2.txt...
[Sequential] Summarizing file3.txt...

=== Running Parallel (AsyncParallelBatchNode) ===
[Parallel] Summarizing file1.txt...
[Parallel] Summarizing file2.txt...
[Parallel] Summarizing file3.txt...

Sequential took: 3.00 seconds
Parallel took:   1.00 seconds
```

## Key Points

- **Sequential**: Total time = sum of all item times
  - Good for: Rate-limited APIs, maintaining order

- **Parallel**: Total time ≈ longest single item time
  - Good for: I/O-bound tasks, independent operations 

## Tech Dive Deep

- **Python's GIL** prevents true CPU-bound parallelism, but LLM calls are I/O-bound
- **Async/await** overlaps waiting time between requests
  - Example: `await client.chat.completions.create(...)`
  - See: [OpenAI's async usage](https://github.com/openai/openai-python?tab=readme-ov-file#async-usage)

For maximum performance and cost efficiency, consider using batch APIs:
- [OpenAI's Batch API](https://platform.openai.com/docs/guides/batch) lets you process multiple prompts in a single request
- Reduces overhead and can be more cost-effective for large workloads