Abstract Nodes for Dynamic System Testing
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Solution Overview
Problem
The challenge lies in testing and optimizing computational systems where certain portions of the computational graph are unavailable or costly to execute, leading to fluctuations in characteristics during testing, making it difficult to explore the impact of compilation settings and other factors.
Innovation Solution
The introduction of abstract nodes that model physical and logical time delays, allowing for the creation of a computational graph that can be analyzed and executed to capture the behavior of concrete nodes, facilitating performance optimization, design space exploration, and system testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If concrete computational nodes are used in the computational graph, then execution accuracy is improved, but execution cost increases and characteristics fluctuate
Solution Approach 1:
The patent creates abstract nodes that are simplified copies of concrete computational nodes. These abstract nodes replicate the essential characteristics (input ports, output ports, time delays) of concrete nodes but with reduced complexity, enabling testing and analysis without incurring the full execution cost of concrete nodes while maintaining sufficient accuracy for optimization purposes.
Solution Approach 2:
The patent introduces abstract nodes as inexpensive, disposable proxies for concrete nodes. These abstract nodes can be created, modified, and discarded easily during the testing and optimization phases, allowing extensive experimentation with computational graph configurations without the high costs associated with executing actual concrete nodes repeatedly.
2Measurement precision
If concrete computational nodes are used in the computational graph, then execution accuracy is improved, but testing stability deteriorates due to characteristic fluctuations
Solution Approach 1:
The abstract nodes serve as stable copies that eliminate the fluctuations inherent in concrete node execution. By using abstract nodes with fixed characteristics (time delays, port configurations) during testing, the patent achieves consistent and repeatable test results, improving stability while maintaining the essential behavioral properties needed for accurate optimization.
3Productivity
If abstract nodes are used to approximate concrete node characteristics, then execution cost is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent implements a dynamic fidelity system where abstract nodes can be configured with varying levels of detail. The fidelity of abstract node approximations can be adjusted dynamically based on the specific testing or optimization needs, allowing users to balance between execution efficiency and measurement precision by selecting appropriate abstraction levels for different scenarios.
Solution Approach 2:
The patent allows modification of abstract node parameters (such as time delays, port configurations, and behavioral characteristics) to adjust the approximation accuracy. By changing these parameters, users can enhance the precision of abstract node representations when higher accuracy is needed, while maintaining execution efficiency benefits when standard approximations suffice.
Data Source
AI summary
A device may receive information specifying a physical time delay of a computational node of a first computational graph. The first computational graph may include a group of computational nodes that are connected in a particular manner, and may represent at least a portion of a dynamic system. The device may further add an abstract node to a second computational graph based on the received information. The abstract node may correspond to the computational node and may implement the physical time delay when the second computational graph is executed. The second computational graph may include a group of abstract nodes that are connected in the particular manner. The device may also execute the second computational graph, and may obtain one or more behavioral characteristics of the at least the portion of the dynamic system based on executing the second computational graph.