Automated Parallel Iterative Code Generation in Graphical Data Flow
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Solution Overview
Problem
Current graphical programming systems require manual effort and are error-prone when attempting to parallelize iterative code for multi-core processing, as they lack automated mechanisms to analyze and transform loop structures for concurrent execution.
Innovation Solution
A system and method for automatically parallelizing iterative graphical program code by analyzing dependence and reduction operations, generating parallelized code portions that can execute concurrently without user intervention, and deploying them to multi-core or multi-processor platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual analysis and transformation of loop structures is performed to parallelize code, then parallel execution capability is achieved, but development time and complexity increase significantly
Solution Approach 1:
The system performs preliminary automatic analysis of loop structures and data dependencies during the compilation or code generation phase, identifying parallelization opportunities before execution. This preliminary action eliminates the need for manual analysis at development time, resolving the contradiction by preparing parallelization in advance through automated tools rather than manual effort.
Solution Approach 2:
The code transformation system enables self-service parallelization by automatically analyzing loop structures, detecting independence between iterations, and generating parallelized code without requiring user intervention. The system serves itself by using built-in analysis capabilities to identify and transform parallelizable patterns, eliminating manual development time while maintaining parallel execution capability.
2Productivity
If manual customization of graphical program code is performed to achieve parallelism, then execution performance improves, but error rate increases
Solution Approach 1:
The system performs self-service code transformation by automatically analyzing loop structures and generating parallelized code without manual intervention. This eliminates human errors in manual customization while preserving execution performance, as the automated system systematically applies correct parallelization transformations based on formal analysis of data dependencies and loop independence.
Solution Approach 2:
The system incorporates feedback mechanisms that analyze the original loop structure, detect parallelization opportunities, verify transformation correctness, and generate optimized parallel code. This feedback loop ensures that performance improvements are achieved through systematic analysis rather than error-prone manual customization, maintaining both high performance and reliability.
3Ease of operation
If loop structures are treated as explicitly sequential, then ease of programming is maintained, but multi-core utilization is poor
Solution Approach 1:
The system performs preliminary automatic parallelization of loop structures during compilation or code generation, converting sequential loops into parallel forms without requiring programmer action. This preliminary transformation maintains ease of programming by allowing developers to write simple sequential loops while automatically achieving multi-core utilization through backend transformations.
Solution Approach 2:
The compilation system provides self-service parallelization by automatically analyzing loop structures and generating multi-core optimized code without user intervention. Developers can continue to program using simple sequential loop constructs while the system autonomously transforms them for parallel execution, maintaining ease of programming while improving multi-core utilization.
4Productivity
If users manually split inputs and loop operations into multiple copies for parallel execution, then multi-core performance improves, but device complexity increases
Solution Approach 1:
The system performs self-service code transformation by automatically splitting loop operations into multiple parallel copies and managing the complexity of coordination and synchronization. This eliminates the need for users to manually create and manage complex parallel structures, achieving multi-core performance while keeping the original code simple and maintainable.
Solution Approach 2:
The system acts as an intermediary between the simple sequential loop code written by users and the complex parallel execution required for multi-core performance. It automatically generates the intermediate parallelized code structure, handling the complexity of task decomposition, synchronization, and resource management, thereby achieving multi-core performance without increasing user-facing code complexity.
Data Source
AI summary
System and method for automatically parallelizing iterative functionality in a data flow program. A data flow program is stored that includes a first data flow program portion, where the first data flow program portion is iterative. Program code implementing a plurality of second data flow program portions is automatically generated based on the first data flow program portion, where each of the second data flow program portions is configured to execute a respective one or more iterations. The plurality of second data flow program portions are configured to execute at least a portion of iterations concurrently during execution of the data flow program. Execution of the plurality of second data flow program portions is functionally equivalent to sequential execution of the iterations of the first data flow program portion.