AI-Driven Software Parallelization for Multi-Core Execution
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Solution Overview
Problem
Existing software applications struggle to efficiently execute on multiprocessor or distributed systems, particularly digital twins, requiring specialized coding and expert skills, and often result in suboptimal performance due to a lack of automated parallelization methods.
Innovation Solution
The method involves intercepting instructions during software execution, using an AI engine with a machine learning model to generate commands for parallel or serial execution on multiple processor cores, facilitating automated distribution and execution without specialized coding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If software applications are manually parallelized for multiprocessor systems, then execution efficiency on parallel processors is improved, but the complexity and difficulty of software development increases significantly
Solution Approach 1:
The system employs AI agents that automatically analyze software applications and generate parallelization strategies without human intervention. The AI engine executes the application, intercepts instructions, and autonomously determines optimal parallel execution plans, making the system self-serve the parallelization task that previously required expert manual intervention.
Solution Approach 2:
The patent replaces the mechanical process of manual software parallelization with an intelligent AI-based system. Instead of software engineers manually analyzing and rewriting code for parallel execution, an AI engine with machine learning models automatically performs the analysis and generates parallelization strategies, substituting human cognitive work with automated intelligent processing.
2Productivity
If expert software engineers manually parallelize applications, then execution performance on distributed systems improves, but the availability of skilled personnel decreases and costs increase
Solution Approach 1:
The AI-powered system performs the specialized task of application parallelization autonomously without requiring expert software engineers. The system serves itself by automatically analyzing applications, determining parallelization strategies, and generating optimized execution plans, eliminating the need for scarce skilled personnel.
Solution Approach 2:
The patent introduces an AI engine as an intermediary between the software application and the parallel processing system. This intermediary automatically translates sequential application code into parallel execution plans, bridging the gap without requiring human experts to perform the complex translation task.
3Ease of manufacture
If a single parallelization strategy is applied to all applications, then implementation simplicity is maintained, but execution optimization for specific applications deteriorates
Solution Approach 1:
The system applies different parallelization strategies tailored to each specific application's characteristics. The AI engine analyzes each application individually and generates customized parallel execution plans that optimize for that application's specific requirements, rather than applying a uniform strategy to all applications.
Solution Approach 2:
The patent implements a dynamic parallelization approach where the AI engine adapts its strategy based on real-time analysis of each application's behavior and characteristics. The system can modify parallelization decisions during execution based on observed performance metrics and application-specific patterns.
Data Source
AI summary
The invention provides for utilization of a machine learning (ML) subsystem to create additional parallel execution on multiple processor cores (CPU, GPU, SPU, etc.) and to further optimize parallel execution within and across nodes of a multi-node system. A method according to the invention comprises executing software on a first processor core to process a set of data, intercepting one or more instructions executed during execution of the software; applying a representation of the intercepted instructions to the ML subsystem to generate action outputs that include commands to effect further execution of the software on multiple processor cores; and responding to those action outputs to so execute the software to process at least portions of the data.