ML-Guided Software Distribution for Multi-Node 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 burdensome and often suboptimal.
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 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 multiple processor cores or distributed systems, then execution speed and productivity are improved, but the complexity of coding and system configuration increases significantly
Solution Approach 1:
The system performs self-parallelization by automatically analyzing the software application and generating parallel execution plans without requiring manual intervention from developers. The parallelization engine autonomously identifies parallelizable tasks, determines optimal distribution across processing elements, and manages synchronization, allowing the system to serve itself rather than requiring expert programmer intervention.
Solution Approach 2:
A parallelization engine acts as an intermediary layer between the software application and the multi-core/distributed system. This intermediary automatically translates sequential code into parallel execution plans, managing the complexity of task distribution, synchronization, and resource allocation, thereby shielding developers from the inherent complexity of parallel programming.
2Productivity
If existing parallelization tools like OpenMP and CUDA are used, then parallel execution capability is improved, but the burden on developers and the suboptimality of results increase
Solution Approach 1:
The system eliminates the need for developers to manually use parallelization tools by implementing automatic parallelization. The parallelization engine autonomously analyzes the application code, identifies parallelizable sections, and generates optimized parallel execution plans without requiring developers to learn or apply tools like OpenMP or CUDA, thereby removing the developer burden entirely.
Solution Approach 2:
The system dynamically adjusts parallelization parameters such as grain size, distribution strategy, and synchronization points based on the specific characteristics of the application and the target hardware architecture. This automated parameter optimization eliminates the need for developers to manually tune these complex parameters, reducing burden while achieving superior performance compared to manual parallelization approaches.
3Adaptability or versatility
If software applications are distributed across heterogeneous processing elements, then adaptability and resource utilization are improved, but the difficulty of managing and coordinating execution increases
Solution Approach 1:
The parallelization engine serves as an intelligent intermediary that automatically manages the distribution of tasks across heterogeneous processing elements. It analyzes the capabilities of different processing elements, optimally allocates tasks to match resources, and handles all coordination and synchronization requirements, thereby simplifying the management of heterogeneous systems while maximizing resource utilization.
Solution Approach 2:
The system dynamically adapts its parallelization strategy based on the specific characteristics of the target heterogeneous system. It automatically detects the capabilities and states of different processing elements and adjusts task distribution, load balancing, and synchronization mechanisms in real-time, enabling effective management of diverse hardware configurations without increasing coordination complexity for developers.
Data Source
AI summary
The invention provides for utilization of a machine learning (ML) subsystem on each node of a multi-node system to effect parallel execution of software applications across multiple nodes through interaction of the ML subsystems and through the exchange of objects (instruction, data, task and thread) that define the parallel execution. A method according to the invention includes executing software on a first processing element, intercepting instructions executed during execution of the software, applying a representation of the intercepted instructions to the ML subsystem to generate action outputs to effect further execution of the software on multiple processing elements, generating objects associated with the software and/or with data to be processed thereby, and making the action outputs and/or object(s) available to multiple processing elements to effect such further execution.