Script-Based 3D CAD Kernel Bridge for Parallel Processing
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Solution Overview
Problem
Computer-aided design (CAD) systems face limitations in processing efficiency when handling complex or large three-dimensional models, leading to slow construction and evaluation of design alternatives due to the demand for significant computer resources.
Innovation Solution
The implementation of script-based techniques that utilize a CAD kernel through a bridge layer, allowing for parallel processing and task prioritization, enabling the creation, modification, and analysis of three-dimensional models without a graphical user interface, and optimizing resource utilization by identifying independent tasks that can be executed independently on distinct processing units.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional CAD systems are used to construct and evaluate complex three-dimensional models, then design accuracy and completeness are maintained, but processing speed and resource efficiency deteriorate
Solution Approach 1:
The patent segments the CAD system into distinct processing units, each handling specific tasks independently. The job set is divided into multiple tasks that can be executed in parallel on different processing units, with each unit managing its own CAD kernel instance. This segmentation enables concurrent processing of model construction, mesh generation, and analysis operations, significantly improving processing speed while maintaining system manageability through modular architecture.
Solution Approach 2:
The patent introduces a bridge layer as an intermediary between the script-based interface and the CAD kernel. This bridge layer translates high-level scripting commands into CAD kernel operations, enabling efficient task management and parallel processing coordination. The intermediary abstracts the complexity of direct CAD kernel interaction, allowing scripts to orchestrate multiple processing units without managing low-level details, thus improving productivity while hiding system complexity.
2Reliability
If significant computer resources are allocated to handle complex models, then model evaluation completeness is improved, but resource utilization efficiency deteriorates
Solution Approach 1:
The patent implements preliminary action by identifying and prioritizing common tasks within the job set before execution. Tasks that are frequently performed or have high impact on model evaluation completeness are prioritized in the processing queue. This preliminary organization ensures that critical evaluation operations are executed efficiently, maintaining model evaluation completeness while optimizing resource allocation by avoiding redundant computations and ensuring high-impact tasks receive adequate computational resources.
Solution Approach 2:
The patent dynamically adjusts processing parameters based on task characteristics and resource availability. The bridge layer monitors the state of processing units and modifies execution parameters such as thread allocation, memory allocation, and task scheduling priorities. This parameter adaptation enables the system to maintain complete model evaluation by allocating sufficient resources to critical tasks while improving overall resource utilization efficiency by optimizing the distribution of computational resources across multiple processing units.
3Measurement precision
If sequential processing is used to ensure task dependency correctness, then design analysis accuracy is maintained, but analysis time increases
Solution Approach 1:
The patent implements dynamic task scheduling that adapts the processing sequence based on task dependencies and completion status. Rather than rigid sequential execution, the system dynamically determines which tasks can be executed in parallel by continuously evaluating dependency relationships. This dynamic approach maintains design analysis accuracy by ensuring that tasks with dependencies are executed in the correct order while allowing independent tasks to run concurrently, thereby reducing overall analysis time without compromising computational correctness.
Solution Approach 2:
The patent incorporates feedback mechanisms where the bridge layer continuously monitors task execution status and dependency satisfaction. Based on this feedback, the system dynamically adjusts the processing queue, promoting tasks that are ready to execute and delaying tasks that depend on incomplete operations. This feedback-driven task management ensures design analysis accuracy by maintaining proper execution order for dependent tasks while maximizing parallel execution opportunities, thus reducing analysis time without sacrificing computational correctness.
Data Source
AI summary
Techniques and systems for creating and performing analysis on three-dimensional computer models are described. The model creation and analysis may be specified using an un-compiled script that includes a set of instructions. The set of instructions may implement a computer-aided design (CAD) kernel using a bridge layer that includes compiled bridge code. The instructions may be used to create a job set having a group of tasks. Using the job set, a processing queue may be constructed, the processing queue having a subset of common tasks prioritized within the processing queue. Subsets or groups of tasks or instructions may also be executed in parallel using multiple distinct processors or a multi-threaded processor.