# Parallel Code Inputting & Analysis

The first step in the Parallel AI process is the input and analysis of existing sequential code or high-level commands provided by the user to identify opportunities for parallel processing. This works in 3 phases:



**Phase 1 - User Input:** Developers write code using a high-level, Python-like syntax that is designed to be intuitive and easy to understand. This code can represent complex algorithms or routine data processing tasks.

**Phase 2 - Code Parsing:** The Parallel AI system parses the input code to understand its structure, dependencies, and the computational goals it aims to achieve. This parsing step is critical as it lays the groundwork for identifying parallelization opportunities.

**Phase 3 - Dependency Analysis:** The system analyzes data dependencies within the code to identify which parts of the code can be executed in parallel and which parts must be executed sequentially. This involves building a dependency graph that visually maps out the tasks and their dependencies.



With the opportunities for parallel processing now identified, the process moves on to Parallel AI’s core function of automatic parallelization.

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