Distributed Object Communication Protocol for AI-Led Parallel Execution
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Solution Overview
Problem
Existing software applications struggle to efficiently execute on multiprocessor or distributed systems, particularly digital twins, due to the complexity of parallelizing tasks across heterogeneous and dynamically changing environments, requiring specialized coding and expert skills, and existing tools like OpenMP and CUDA are cumbersome to use.
Innovation Solution
The method employs an AI engine with a machine learning model to intercept instructions, generate action outputs, and automate the distribution and execution of software applications across multiple processor cores or elements, facilitating parallel or serial execution without specialized coding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If software applications are parallelized across multiprocessor systems using traditional methods (OpenMP, CUDA, manual parallelization), then execution speed and resource utilization improve, but the complexity of coding and implementation increases significantly
Solution Approach 1:
The system employs AI agents that automatically analyze application code, identify parallelization opportunities, and generate optimized distributed execution plans without requiring manual intervention from developers. The AI engine self-services the complex task of parallelization by interpreting high-level code descriptions and autonomously determining task distribution strategies across heterogeneous processors.
Solution Approach 2:
An AI-based intermediary layer is introduced between the application code and the multiprocessor system. This intermediary automatically translates high-level code descriptions into optimized parallel execution plans, managing the complexity of task distribution, data movement, and resource allocation without exposing these complexities to the application developer.
2Productivity
If software applications are distributed across heterogeneous processors, then resource utilization and execution efficiency improve, but the difficulty of managing and coordinating tasks increases
Solution Approach 1:
The system dynamically adapts task distribution strategies based on real-time processor availability, workload characteristics, and performance metrics. AI agents continuously monitor system state and adjust execution plans to optimize resource utilization across heterogeneous processors, handling the complexity of dynamic coordination automatically.
Solution Approach 2:
The system implements feedback mechanisms where AI agents monitor execution performance and use this information to refine task distribution decisions. Performance metrics from distributed execution feed back into the AI engine, which adjusts future task allocation strategies to improve resource utilization and coordination efficiency.
3Ease of operation
If automated AI-based parallelization is implemented, then ease of use and accessibility improve, but the computational overhead for analysis and planning increases
Solution Approach 1:
The system performs preliminary analysis of application code structure and parallelization opportunities before actual execution. AI agents pre-compute task decomposition strategies and execution plans during compilation or load time, reducing the computational overhead during runtime execution while maintaining ease of use.
Data Source
AI summary
An abstract object-oriented communication protocol for a multiple node system to enable distribution of objects (thread, task, data, instruction) required for parallel computing of single or multiple applications amongst nodes across any standard network. Methods according to the invention include transmitting on the network (a) block-type objects associated with instructions making up software to be executed, data-type objects associated with data to be processed, (iii) task-type objects associating block and data objects, and thread-type objects defining a processing environment in which the data of a data-type object is to be processed by software associated with a block-type object.