Distributed Code Execution With Dependency-Based Parallel Task Marking
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Solution Overview
Problem
Current implementations of Large Language Model (LLM) code generation are limited to serial code execution on a single computing device, which restricts their applicability in distributed networks, and fail to optimize for efficient execution across multiple devices.
Innovation Solution
A method and system that analyze code dependencies to identify parallel execution opportunities, mark serial code with indicators for distribution across multiple computing devices, and execute the code in parallel using an execution engine to coordinate tasks across these devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If LLM code generation focuses on serial code execution on a single computing device, then code correctness is achieved, but execution efficiency and applicability to distributed networks are limited
Solution Approach 1:
The system segments the generated serial code into multiple parallel tasks that can be distributed across different computing devices. The code is divided into independent executable units that maintain correctness while enabling parallel execution, thus resolving the contradiction between code reliability and execution efficiency.
Solution Approach 2:
The system transitions from single-device serial execution to multi-device parallel execution by adding the dimension of spatial distribution. Code tasks are allocated across multiple computing devices, transforming the execution model from one-dimensional sequential processing to multi-dimensional parallel processing, thereby improving productivity while maintaining reliability.
2Ease of manufacture
If code is optimized for serial execution on a single device, then implementation simplicity is maintained, but applicability to distributed networks is reduced
Solution Approach 1:
The system creates a universal code execution framework that can operate in both serial and parallel modes, and adapt to different network configurations. The generated code includes markers and metadata that enable it to function across diverse distributed network environments, thus improving adaptability while maintaining implementation simplicity through a unified approach.
Solution Approach 2:
The system dynamically adapts code execution based on the target environment. Code generated by the LLM includes dynamic markers that allow the execution engine to automatically adjust between serial and parallel execution modes depending on the distributed network configuration, enhancing versatility without complicating the implementation.
3Reliability
If LLM generates only correct working code without parallelization, then code quality is ensured, but runtime performance on distributed infrastructure is suboptimal
Solution Approach 1:
The system performs preliminary analysis and marking of code during the generation phase to identify parallelization opportunities. The LLM generates code with embedded markers that pre-identify tasks suitable for parallel execution, so that during runtime, these pre-marked tasks can be executed in parallel without compromising code quality, thereby reducing runtime loss.
Solution Approach 2:
The system introduces an execution engine as an intermediary between the generated serial code and the distributed computing infrastructure. This engine interprets the markers in the code, orchestrates parallel task distribution, and manages execution across multiple devices, thus maintaining code quality while significantly reducing runtime through efficient parallelization.
Data Source
AI summary
Systems and methods for generating and executing distributed code. The systems and methods include analyzing code dependencies in a serial code with a trained model to evaluate opportunities to implement tasks in parallel and marking the serial code with indicators to designate portions of the serial code that can be performed on a plurality of computing devices. The methods and systems further include distributing the portions of the serial code to the plurality of computing devices and executing the serial code in parallel across the plurality of computing devices using an execution engine to coordinate execution across the computing devices.