Dynamic Interval Compilation for AI Application Optimization
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Solution Overview
Problem
Existing methods for executing programs, especially in artificial intelligence applications, face challenges in optimization and parallelization due to the limited size of intermediate representations, leading to inefficient resource utilization and potential performance degradation.
Innovation Solution
A method is introduced where intermediate representations are generated and compiled until the number of operation nodes reaches a dynamically determined interval value, allowing for efficient optimization and parallelization, and the interval value is adjusted based on application characteristics, such as the presence of response waiting operations, to minimize idle time and resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If intermediate representation is generated until a large number of operation nodes are accumulated, then optimization and parallelization effectiveness is improved, but resource consumption and compilation time increase
Solution Approach 1:
The patent segments the intermediate representation generation into batches, where compilation is triggered when the number of operation nodes reaches predetermined interval values. This segmentation allows the system to process intermediate representations in manageable chunks rather than accumulating all nodes at once, thereby reducing resource consumption while maintaining optimization effectiveness.
Solution Approach 2:
The patent dynamically adjusts the interval value for triggering compilation based on the current state of intermediate representation generation. The interval value can be modified according to the complexity and size of the application, allowing the system to adapt to varying resource requirements and optimization needs in real-time.
2Productivity
If intermediate representation is generated in large batches, then optimization effectiveness is improved, but compilation time and resource utilization increase
Solution Approach 1:
The patent divides the intermediate representation generation process into smaller batches triggered at predetermined interval values. This segmentation reduces the size of each compilation task, thereby reducing compilation time and allowing for more frequent, smaller optimization cycles rather than one large time-consuming compilation.
Solution Approach 2:
The system performs preliminary generation of intermediate representations and stops generation when reaching interval values, allowing compilation to be performed on partially completed representations. This preliminary action enables optimization to occur more frequently with smaller datasets, reducing overall compilation time.
3Ease of manufacture
If fixed interval value is used for stopping generation, then implementation simplicity is improved, but adaptability to different application characteristics deteriorates
Solution Approach 1:
The patent introduces dynamic adjustment of the interval value based on application characteristics. The system can modify the interval value according to factors such as the type of application, the complexity of operations, and resource availability, thereby adapting to different scenarios while maintaining a relatively simple implementation framework.
Solution Approach 2:
The patent allows changing the interval value parameter based on the specific characteristics of the application being compiled. By adjusting this key parameter, the system can adapt to different application types and complexity levels without requiring complete redesign, thus achieving versatility through parameter modification.
Data Source
AI summary
The disclosure relates to a method for compiling an application, in which the method is executed by one or more processors, and includes initiating generating a first intermediate representation for a first part of an application, while generating the first intermediate representation, stopping generating the first intermediate representation if it is determined that a number of operation nodes included in the first intermediate representation reaches an interval value, and compiling the generated first intermediate representation.